A novel approach for prediction of vitamin D status using support vector regression

BACKGROUND Epidemiological evidence suggests that vitamin D deficiency is linked to various chronic diseases. However direct measurement of serum 25-hydroxyvitamin D (25(OH)D) concentration, the accepted biomarker of vitamin D status, may not be feasible in large epidemiological studies. An alternative approach is to estimate vitamin D status using a predictive model based on parameters derived from questionnaire data. In previous studies, models developed using Multiple Linear Regression (MLR) have explained a limited proportion of the variance and predicted values have correlated only modestly with measured values. Here, a new modelling approach, nonlinear radial basis function support vector regression (RBF SVR), was used in prediction of serum 25(OH)D concentration. Predicted scores were compared with those from a MLR model. METHODS Determinants of serum 25(OH)D in Caucasian adults (n = 494) that had been previously identified were modelled using MLR and RBF SVR to develop a 25(OH)D prediction score and then validated in an independent dataset. The correlation between actual and predicted serum 25(OH)D concentrations was analysed with a Pearson correlation coefficient. RESULTS Better correlation was observed between predicted scores and measured 25(OH)D concentrations using the RBF SVR model in comparison with MLR (Pearson correlation coefficient: 0.74 for RBF SVR; 0.51 for MLR). The RBF SVR model was more accurately able to identify individuals with lower 25(OH)D levels (<75 nmol/L). CONCLUSION Using identical determinants, the RBF SVR model provided improved prediction of serum 25(OH)D concentrations and vitamin D deficiency compared with a MLR model, in this dataset.Dr. Guo is funded by an Australian Postgraduate Award. Prof. Lucas is funded by a National Health and Medical Research (NHMRC) Career Development Fellowship and receives research funding from Cancer Australia, NHMRC, and MS Research Australia. Prof. Ponsonby is funded by a NHMRC Research Fellowship and receives research funding from NHMRC and MS Research Australia. The Ausimmune Study was funded by the US National Multiple Sclerosis Society, NHMRC, and MS Research Australia

Cardiovascular disease in East Asian immigrants living in Australia: considerations in relation to vitamin D deficiency, smoking and acculturation

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in Australia and accounts for the second highest disease burden in disability-adjusted life years. Meanwhile, according to a recent report 28.1% of the estimated resident population of Australia was born overseas; this is the highest proportion of immigrants in the past 120 years. Patterns of CVD risk, incidence, and mortality vary significantly across different ethnic population groups. This means that the demographic change in the Australian population due to overseas immigration is likely to alter patterns of CVD in terms of incidence, prevalence, and mortality in both the short and long term. These changes may challenge existing health policies, models of service, and guidelines for prevention and care in Australia. Therefore, it is now important to understand the risk for major CVD and risk factor profiles in immigrants compared to the Australian-born population, and how these factors change according to acculturation. This thesis specifically aims to better understand risk factor profiles and CVD in East Asian immigrants and the effect of increasing acculturation. METHODS: The thesis applied a variety of research methods to address these research aims. First, a systematic review of the literature and meta-analysis was performed to investigate the prevalence of smoking in East Asian populations living in western countries, and then to quantify the effect size for the association between acculturation and smoking prevalence in these populations. Second, a new cross-sectional study was designed and completed, and the data analysed in order to investigate and assess the factors related to vitamin D status, as a possible CVD risk factor, in East Asians living in Canberra. Third, I examined whether mathematical models used for the prediction of vitamin D status were valid and tested the accuracy of different ways of modelling the data to improve prediction, using data already collected in a case-control study as well as published data from the National Health and Nutrition Examination study. Last, an analysis of data from a population-based cohort study that was linked to hospital admissions and mortality records was conducted in order to assess CVD risk profiles according to region of birth and acculturation level and to investigate hospitalisation for CVD as East Asian immigrants become acculturated to the host country. RESULTS and DISCUSSION: The systematic review and meta-analysis of cross-sectional studies showed that East Asian-born women were far less likely to smoke than East Asian-born men and Australian-born individuals. The prevalence of smoking in East Asian-born men was high compared to western-born counterparts and smoking cessation was uncommon. However, the prevalence of current smoking was generally lower in men, but higher in women, compared to that of the native country and in association with longer duration of residence. Nevertheless, analysis of baseline cross-sectional data from the population-based 45 and Up Study, in Australia, showed that the prevalence of current smoking among Asian-born men was about the same as their Australian-born counterparts, and increased in relation to longer duration of residence. This contradicts the findings of the meta-analysis, and may be specific to Australia or specific to the 45 and Up Study, where the questionnaire was offered only in English, so that less acculturated immigrants may not have participated. The cross-sectional Asian Australian Health Study, based in Canberra, revealed that vitamin D deficiency in East Asian-born immigrants was common, and greater acculturation was associated with higher vitamin D status in this population. Higher vitamin D status was associated with a lower risk of hypercholesterolemia, but not other markers of cardiometabolic ill-health in this study. Because of the cross-sectional nature of the study, it is not possible to assess whether this is a causal association; it appeared to be mediated by physical activity. The studies testing the validity of prediction models for vitamin D status, as used in large health studies, showed that these may have poor prediction accuracy and a high risk of bias due to incorrect use of instrumental variables in the modelling. Furthermore, support vector regression modelling was shown to provide more accurate prediction of vitamin D status compared to multiple linear regression. The analysis of linked data from the population-based 45 and Up cohort study indicated that CVD risk factor profiles of East Asian immigrants tended to approximate those of Australian-born with increasing levels of acculturation. The association between region of birth and age at immigration to CVD risk varied across different types of CVD and was likely to be determined by a complex interaction of factors related to both the host country and the country of origin. CONCLUSIONS: This thesis explored the association between acculturation, putative CVD risk factors, CVD related hospitalisation, and all-cause mortality in East-Asian-born immigrants to western countries, mainly Australia. The risk of incident CVD incident is lower in EastAsian immigrant populations than in the Australian-born population. However, changes in the prevalence of various risk factors with increasing acculturation suggest that the pattern of CVD risk in Asian immigrants will change toward that of the Australian-born population over the coming years, as these immigrants become acculturated and adopt unhealthy diets and women are more likely to smoke, but there are healthier patterns of physical activity. Having identified these trends with acculturation, there are real opportunities, with targeted, culturally appropriate health promotion materials, to maximise the opportunities to make the transition to Australia one that improves, rather than detracts from, the health of this growing immigrant group

A new approach for the implementation of contact line motion based on the phase-filed lattice Boltzmann method

This paper proposes a new strategy to implement the free-energy based wetting boundary condition within the phase-field lattice Boltzmann method. The greatest advantage of the proposed method is that the implementation of contact line motion can be significantly simplified while still maintaining good accuracy. For this purpose, the liquid-solid free energy is treated as a part of the chemical potential instead of the boundary condition, thus avoiding complicated interpolations with irregular geometries. Several numerical testing cases including the droplet spreading processes on the idea flat, inclined and curved boundaries are conducted, and the results demonstrate that the proposed method has good ability and satisfactory accuracy to simulate contact line motions

Tightrope walking: Using predictors of 25 (OH)D concentration based on multivariable linear regression to infer associations with health risks

The debate on the causal association between vitamin D status, measured as serum concentration of 25-hydroxyvitamin D (25[OH]D), and various health outcomes warrants investigation in large-scale health surveys. Measuring the 25(OH)D concentration for each participant is not always feasible, because of the logistics of blood collection and the costs of vitamin D testing. To address this problem, past research has used predicted 25(OH)D concentration, based on multivariable linear regression, as a proxy for unmeasured vitamin D status. We restate this approach in a mathematical framework, to deduce its possible pitfalls. Monte Carlo simulation and real data from the National Health and Nutrition Examination Survey 2005-06 are used to confirm the deductions. The results indicate that variables that are used in the prediction model (for 25[OH]D concentration) but not in the model for the health outcome (called instrumental variables), play an essential role in the identification of an effect. Such variables should be unrelated to the health outcome other than through vitamin D; otherwise the estimate of interest will be biased. The approach of predicted 25(OH)D concentration derived from multivariable linear regression may be valid. However, careful verification that the instrumental variables are unrelated to the health outcome is required

GRASS: Unified Generation Model for Speech Semantic Understanding

This paper explores the instruction fine-tuning technique for speech semantic understanding by introducing a unified end-to-end (E2E) framework that generates semantic labels conditioned on a task-related prompt for audio data. We pre-train the model using large and diverse data, where instruction-speech pairs are constructed via a text-to-speech (TTS) system. Extensive experiments demonstrate that our proposed model significantly outperforms state-of-the-art (SOTA) models after fine-tuning downstream tasks. Furthermore, the proposed model achieves competitive performance in zero-shot and few-shot scenarios. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code

Towards Explainable Conversational Recommender Systems

Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which poses further challenges for explanations. To better measure explainability in conversational recommender systems (CRS), we propose ten evaluation perspectives based on concepts from conventional recommender systems together with the characteristics of CRS. We assess five existing CRS benchmark datasets using these metrics and observe the necessity of improving the explanation quality of CRS. To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with over 2,000 high-quality rewritten explanations. We compare two baseline approaches to perform explanation generation based on E-ReDial. Experimental results suggest that models trained on E-ReDial can significantly improve explainability while introducing knowledge into the models can further improve the performance. GPT-3 in the in-context learning setting can generate more realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial can better generate clear reasons for recommendations based on user preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial

A well-balanced lattice Boltzmann model for binary fluids based on the incompressible phase-field theory

Spurious velocities arising from the imperfect offset of the undesired term at the discrete level are frequently observed in numerical simulations of equilibrium multiphase flow systems using the lattice Boltzmann equation (LBE) method. To capture the physical equilibrium state of two-phase fluid systems and eliminate spurious velocities, a well-balanced LBE model based on the incompressible phase-field theory is developed. In this model, the equilibrium distribution function for the Cahn-Hilliard (CH) equation is designed by treating the convection term as a source to avoid the introduction of undesired terms, enabling achievement of possible discrete force balance. Furthermore, this approach allows for the attainment of a divergence-free velocity field, effectively mitigating the impact of artificial compression effects and enhancing numerical stability. Numerical tests, including a flat interface problem, a stationary droplet, and the coalescence of two droplets, demonstrate the well-balanced properties and improvements in the stability of the present model

A Multikernel-Like Learning Algorithm Based on Data Probability Distribution

In the machine learning based on kernel tricks, people often put one variable of a kernel function on the given samples to produce the basic functions of a solution space of learning problem. If the collection of the given samples deviates from the data distribution, the solution space spanned by these basic functions will also deviate from the real solution space of learning problem. In this paper a multikernel-like learning algorithm based on data probability distribution (MKDPD) is proposed, in which the parameters of a kernel function are locally adjusted according to the data probability distribution, and thus produces different kernel functions. These different kernel functions will generate different Reproducing Kernel Hilbert Spaces (RKHS). The direct sum of the subspaces of these RKHS constitutes the solution space of learning problem. Furthermore, based on the proposed MKDPD algorithm, a new algorithm for labeling new coming data is proposed, in which the basic functions are retrained according to the new coming data, while the coefficients of the retrained basic functions remained unchanged to label the new coming data. The experimental results presented in this paper show the effectiveness of the proposed algorithms