A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, 3D structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.

Kernel Cox partially linear regression: Building predictive models for cancer patients' survival

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high-dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high-dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.

Impact of the digital economy on total factor energy efficiency: evidence from 268 Chinese cities

Due to the advancement of digital technology, the digital economy has developed rapidly, profoundly changing human production and lifestyles, thereby promoting the dual digital transformation of the energy supply and demand sides and having a profound impact on energy utilization efficiency. Based on measuring the total factor energy efficiency (TFEE) of 268 cities in China from 2011 to 2019, we analyze the total and indirect effects of the digital economy on TFEE using a mediated effects model and examine the effects of urban heterogeneity from the perspectives of geographical location, city size, and resource endowment. The results show that the digital economy has a significant positive contribution to TFEE. In addition, the digital economy can promote TFEE through industrial structure upgrading, technological innovation, and environmental regulation. The test results of the subsample show that there is significant heterogeneity in the impact and mechanism of action of the digital economy on TFEE in different geographical locations, city sizes, and resource endowments. By understanding how the digital economy impacts TFEE, policymakers can formulate effective policies to simultaneously accelerate digital economy development and improve TFEE.

Subgroup analysis using Bernoulli-gated hierarchical mixtures of experts models

When it is suspected that the treatment effect may only be strong for certain subpopulations, identifying the baseline covariate profiles of subgroups who benefit from such a treatment is of key importance. In this paper, we propose an approach for subgroup analysis by firstly introducing Bernoulli-gated hierarchical mixtures of experts (BHME), a binary-tree structured model to explore heterogeneity of the underlying distribution. We show identifiability of the BHME model and develop an EM-based maximum likelihood method for optimization. The algorithm automatically determines a partition structure with optimal prediction but possibly suboptimal in identifying treatment effect heterogeneity. We then suggest a testing-based postscreening step to further capture effect heterogeneity. Simulation results show that our approach outperforms competing methods on discovery of differential treatment effects and other related metrics. We finally apply the proposed approach to a real dataset from the Tennessee's Student/Teacher Achievement Ratio project.

Semiparametric probit regression model with misclassified current status data

Current status data arise when each subject under study is examined only once at an observation time, and one only knows the failure status of the event of interest at the observation time rather than the exact failure time. Moreover, the obtained failure status is frequently subject to misclassification due to imperfect tests, yielding misclassified current status data. This article conducts regression analysis of such data with the semiparametric probit model, which serves as an important alternative to existing semiparametric models and has recently received considerable attention in failure time data analysis. We consider the nonparametric maximum likelihood estimation and develop an expectation-maximization (EM) algorithm by incorporating the generalized pool-adjacent-violators (PAV) algorithm to maximize the intractable likelihood function. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semiparametrically efficient. Furthermore, the numerical results in simulation studies indicate that the proposed method performs satisfactorily in finite samples and outperforms the naive method that ignores misclassification. We then apply the proposed method to a real dataset on chlamydia infection.

Forecasting the renewable energy consumption of Australia by a novel grey model with conformable fractional opposite-direction accumulation

The accurate prediction of renewable energy consumption (REC) is of great significance to ensure energy security, reduce dependence on fossil energy, and promote sustainable economic and social development. In this paper, a novel grey model with conformable fractional opposite-direction accumulation (CFOA), abbreviated as the CFOGM (1,1) model, is proposed to forecast REC in Australia. The new model is discussed in detail with a new CFOA operation and the GM (1,1) model and can take full advantage of the information carried by the original data. The CFOGM (1,1) model has lower modeling error and better fitting and forecasting accuracy than other grey, Holt, and ARM models and can better capture the change trend of REC and achieve accurate prediction. The forecasting results present that the REC in Australia is 497-581 petajoules in 2021, 596-728 petajoules in 2022, and 715-912 petajoules in 2023, indicating that the REC in Australia is still accelerating.

Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution

We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for n -estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires the existence of a function as a solution to a representer equation that connects the shadow variable to the mean functional. Solutions to the representer equation may not be unique, which presents substantial challenges for non-parametric estimation, and standard theories for non-parametric sieve estimators are not applicable here. We construct a consistent estimator of the solution set and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator of an appropriately chosen solution. The estimator is asymptotically normal, locally efficient and attains the semi-parametric efficiency bound under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing.

Vaccination in the childhood and awareness of basic public health services program among internal migrants: a nationwide cross-sectional study

BACKGROUND

Vaccination is proved to be one of the most effective and efficient way to prevent illness and reduce health inequality. Studies about association between vaccination inequalities in the childhood and awareness of basic public health services program among internal migrants in China are lacking. In this study, we aimed to explore the association between migrants' vaccination status between 0 and 6 years old and their awareness of the National Basic Public Health Services (BPHSs) project in China.

METHODS

We included 10,013 respondents aged 15 years old or above of eight provinces from 2017 Migrant Population Dynamic Monitoring Survey in China, a nationwide cross-sectional study. Univariate and multivariable logistic regressions were used to assess vaccination inequalities and the awareness of public health information.

RESULTS

Only 64.8% migrants were vaccinated in their childhood, which is far below the goal of national requirement of 100% vaccination. This also indicated the vaccination inequalities among migrants. Female, the middle-aged, married or having a relationship, the highly educated and the healthy population had higher awareness of this project than others. Both univariate and multivariate logistic regressions showed greatly significant association between vaccination status and some vaccines. Specifically, after adding convariates, the results showed that there were significant associations between the vaccination rates of eight recommended vaccines in the childhood and their awareness of BPHSs project (all p values < 0.001), including HepB vaccine (OR: 1.28; 95%CI: 1.19, 1.37), HepA vaccine (OR: 1.27; 95%CI: 1.15, 1.41), FIn vaccine (OR: 1.28; 95%CI: 1.16, 1.45), JE vaccine (OR: 1.14; 95%CI: 1.04, 1.27), TIG vaccine (OR: 1.27; 95%CI: 1.05, 1.47), DTaP vaccine (OR: 1.30; 95%CI: 1.11-1.53), MPSV vaccine (OR: 1.26; 95%CI: 1.07-1.49), HF vaccine (OR: 1.32; 95%CI: 1.11, 1.53), except for RaB vaccine (OR: 1.07; 95%CI: 0.89, 1.53).

CONCLUSIONS

The vaccination inequalities exist among migrants. There is a strong relationship between the vaccination status in the childhood and the awareness rate of BPHSs project among migrants. From our findings we could know that the promotion of vaccination rates of the disadvantaged population such as the internal migrants or other minority population can help them increase the awareness of free public health services, which was proved to be beneficial for health equity and effectiveness and could promote public health in the future.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration

In this paper, we empirically study the spatial association network of PM_2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM_2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM_2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM_2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM_2.5. The greater the urbanization differences, the more likely the generation of PM_2.5 links is, while the opposite is true for differences in geographical distance.

Association between early life exposure to the great famine and possible sarcopenia in older Chinese adults: a national cross-sectional study

OBJECTIVES

We used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate how an early life famine exposure affected possible sarcopenia (PS) and to explore the extent to which a sex difference exists in the association among older Chinese adults, as well as whether risk factors modify the association.

DESIGN

Cross-sectional study.

SETTING

28 provinces of China.

PARTICIPANTS

Considering that the Great Chinese Famine lasted from the spring of 1959 to the fall of 1961, 3557 participants were selected and categorised into four subgroups based on their date of birth: unexposed group (1 October 1962 to 30 September 1964), fetal exposed group (1 October 1959 to 30 September 1961), infant exposed group (1 January 1958 to 31 December 1958) and preschool exposed group (1 January 1956 to 31 December 1957).

OUTCOME MEASURE

PS was defined as having low muscle strength or low physical performance.

METHODS

We used multivariable logistic models to analyse the association between early life famine exposure and the risk of PS in elderly life.

RESULTS

The prevalences of PS among individuals in the unexposed, fetal, infant and preschool exposed groups were 15.1%, 14.4%, 23.6% and 21.9%, respectively. Compared with the unexposed group, the infant (OR: 1.55; 95% CI 1.17 to 2.05) and preschool exposed (OR: 1.46; 95% CI 1.17 to 1.82) groups exhibited significantly higher risks of PS. In men, the infant (OR: 2.15; 95% CI 1.40 to 3.31) and preschool exposed (OR: 1.78; 95% CI 1.23 to 2.57) groups were more likely to have PS, but no significant increase was seen in women. In both sexes, prevalence of PS was unrelated to early life famine exposure in the urban, underweight and normal weight subgroups.

CONCLUSIONS

Early life exposure to the Great Chinese Famine was associated with a higher risk of PS in older adults. Keeping normal nutritional status in elderly life might help avoid the risk of PS, whatever the effect of early famine exposure.

Iterative deep neural networks based on proximal gradient descent for image restoration

The algorithm unfolding networks with explainability of algorithms and higher efficiency of Deep Neural Networks (DNN) have received considerable attention in solving ill-posed inverse problems. Under the algorithm unfolding network framework, we propose a novel end-to-end iterative deep neural network and its fast network for image restoration. The first one is designed making use of proximal gradient descent algorithm of variational models, which consists of denoiser and reconstruction sub-networks. The second one is its accelerated version with momentum factors. For sub-network of denoiser, we embed the Convolutional Block Attention Module (CBAM) in previous U-Net for adaptive feature refinement. Experiments on image denoising and deblurring demonstrate that competitive performances in quality and efficiency are gained by compared with several state-of-the-art networks for image restoration. Proposed unfolding DNN can be easily extended to solve other similar image restoration tasks, such as image super-resolution, image demosaicking, etc.

Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

Crude oil is the most important energy source in the world, and fluctuations in oil prices can significantly influence investors, companies, and governments. However, crude oil prices have numerous characteristics, including randomness, sudden structural changes, intrinsic nonlinearity, volatility, and chaotic nature. This makes the accurate forecasting of crude oil prices a difficult and challenging task. In this paper, a hybrid prediction model for crude oil futures prices is proposed, the accuracy and robustness of which are demonstrated via controlled experiments and sensitivity analysis. This study uses a new data denoising method for data processing to improve the accuracy and stability of the predictions of crude oil prices. Furthermore, the chaotic time-series prediction method, shallow neural networks, linear model prediction methods, and deep learning methods are adopted as submodels. The results of interval forecasts with narrow widths and high prediction accuracies are derived by introducing a confidence interval adjustment coefficient. The results of the simulation experiments indicate that the proposed hybrid prediction model exhibits higher accuracy and efficiency, as well as better robustness of the forecasting than the control models. In summary, the proposed forecasting framework can derive accurate point and interval forecasts and provide a valuable reference for the price forecasting of crude oil futures.

Internal migration and child health: An investigation of health disparities between migrant children and left-behind children in China

Using data from the China Education Panel Survey (CEPS), this study empirically examines the association between internal migration and child health through an investigation of health disparities between migrant children and left-behind children in China. The results show that, in comparison with being left behind, migrating with parents significantly improves children’s self-reported health, height-for-age z-score (HAZ) and BMI-for-age z-score (BAZ), and reduces their frequency of sickness. These findings remain robust to a suite of robustness checks. Furthermore, the health effects of internal migration are more prominent for children with a rural hukou compared with urban ones. Although migrant children are more likely to experience teacher discrimination, they have higher levels of parental care, family relationships, and peer relationships relative to their left-behind counterparts, which indicates possible mechanisms behind the association between children’s migration and health. Our findings underline the importance of policy improvement and evidence-based interventions aiming at reducing involuntary parent-child separation and facilitating the development in health of disadvantaged children in developing countries like China.

Contributions of park constructions to residents' demands of ecosystem services consumption: A case study of urban public parks in Beijing

Urban public parks can provide convenience for residents to get close to nature and provide places for daily ecosystem services. It is of practical and theoretical significance to choose urban public parks as the entry point to explore the changing trends and supply paths of urban residents' daily ecosystem service consumption. Based on the government 's research? of urban public parks in Beijing from 1993 to 2018, this study explores the residents' ecosystem services consumption demands and the contributions of park constructions to these demands. The results show that: (1) in the past 25 years, the frequency, duration, participation rate, and evaluation of people's daily ecosystem service consumption have increased significantly. In other words, the ecosystem services demands are increasing. (2) different constructions of a park have distinct contributions to the increasing demands of ecosystem service consumption. The contributions from constructions of the natural landscape and the infrastructure have been in decline since 1993, yet they contribute the most to the demands of residents' ecosystem services consumption until 2018. The contributions made by constructions of management and maintenance, and transportation around urban public parks have been on the rise and the significant points occurring after the 2008 Olympic Games. Our research proposes a method to determine the relation between the demands of residents' ecosystem services consumption and the contributions of park constructions to these demands, which has significant implications for optimizing the constructions of urban public parks to better meet the demands of ecosystem services consumption.