Showing papers by "Runze Li published in 2022"

Electrostatic atomization minimum quantity lubrication machining: from mechanism to application

Abstract: Metal cutting fluids (MCFs) under flood conditions do not meet the urgent needs of reducing carbon emission. Biolubricant-based minimum quantity lubrication (MQL) is an effective alternative to flood lubrication. However, pneumatic atomization MQL has poor atomization properties, which is detrimental to occupational health. Therefore, electrostatic atomization MQL requires preliminary exploratory studies. However, systematic reviews are lacking in terms of capturing the current research status and development direction of this technology. This study aims to provide a comprehensive review and critical assessment of the existing understanding of electrostatic atomization MQL. This research can be used by scientists to gain insights into the action mechanism, theoretical basis, machining performance, and development direction of this technology. First, the critical equipment, eco-friendly atomization media (biolubricants), and empowering mechanisms of electrostatic atomization MQL are presented. Second, the advanced lubrication and heat transfer mechanisms of biolubricants are revealed by quantitatively comparing MQL with MCF-based wet machining. Third, the distinctive wetting and infiltration mechanisms of electrostatic atomization MQL, combined with its unique empowering mechanism and atomization method, are compared with those of pneumatic atomization MQL. Previous experiments have shown that electrostatic atomization MQL can reduce tool wear by 42.4% in metal cutting and improve the machined surface R a by 47% compared with pneumatic atomization MQL. Finally, future development directions, including the improvement of the coordination parameters and equipment integration aspects, are proposed.

Daily food insecurity is associated with diet quality, but not energy intake, in winter and during COVID-19, among low-income adults

Abstract: Food insecurity (FI) is a dynamic phenomenon. Experiences of daily FI may impact dietary outcomes differently within a given month, across seasons, and before or during the COVID-19 pandemic.The aims of this study were to investigate the association of short-term FI with dietary quality and energy 1) over six weeks in two seasonal months and 2) before and during the COVID-19 pandemic.Using an ecological momentary assessment framework on smartphones, this study tracked daily FI via the 6-item U.S. Adult Food Security Survey Module and dietary intake via food diaries in 29 low-income adults. A total of 324 person-days of data were collected during two three-week long waves in fall and winter months. Generalized Estimating Equation models were applied to estimate the daily FI-diet relationship, accounting for intrapersonal variation and covariates.A one-unit increase in daily FI score was associated with a 7.10-point (95%CI:-11.04,-3.15) and 3.80-point (95%CI: -6.08,-1.53) decrease in the Healthy Eating Index-2015 (HEI-2015) score in winter and during COVID-19, respectively. In winter months, a greater daily FI score was associated with less consumption of total fruit (-0.17 cups, 95% CI: -0.32,-0.02), whole fruit (-0.18 cups, 95%CI: -0.30,-0.05), whole grains (-0.57 oz, 95%CI: -0.99,-0.16) and higher consumption of refined grains (1.05 oz, 95%CI: 0.52,1.59). During COVID-19, elevated daily FI scores were associated with less intake of whole grains (-0.49 oz, 95% CI: -0.88,-0.09), and higher intake of salt (0.34 g, 95%CI: 0.15,0.54). No association was observed in fall nor during the pre-COVID-19 months. No association was found between daily FI and energy intake in either season, pre-COVID 19, or during-COVID-19 months.Daily FI is associated with compromised dietary quality in low-income adults in winter months and during the COVID-19 period. Future research should delve into the underlying factors of these observed relationships.

Integration of Transcriptome and Metabolome Provides New Insights to Flavonoids Biosynthesis in Dendrobium huoshanense

Abstract: Dendrobium huoshanense is both a traditional herbal medicine and a plant of high ornamental and medicinal value. We used transcriptomics and metabolomics to investigate the effects of growth year on the secondary metabolites of D. huoshanense stems obtained from four different years of cultivation. In this study, a total of 428 differentially accumulated metabolites (DAMs) and 1802 differentially expressed genes (DEGs) were identified. The KEGG enrichment analysis of DEGs and DAMs revealed significant differences in “Flavonoid biosynthesis”, “Phenylpropanoid biosynthesis” and “Flavone and flavonol biosynthesis”. We summarize the biosynthesis pathway of flavonoids in D. huoshanense, providing new insights into the biosynthesis and regulation mechanisms of flavonoids in D. huoshanense. Additionally, we identified two candidate genes, FLS (LOC110107557) and F3’H (LOC110095936), which are highly involved in flavonoid biosynthesis pathway, by WGCNA analysis. The aim of this study is to investigate the effects of growth year on secondarily metabolites in the plant and provide a theoretical basis for determining a reasonable harvesting period for D. huoshanense.

A new pixel-to-object method for evaluating the capability of the GPM IMERG product to quantify precipitation systems

Abstract: • A pixel-to-object method is proposed to evaluate satellite-derived precipitation system performance based on gauge data. • Dependency of IMERG bias on the precipitation system scale and position in the systems is revealed. • IMERG generally overestimates the precipitation system sizes, which is more severe for small systems. One of the major advantages of satellite precipitation estimates is that they can provide a complete picture of precipitation systems; a difficult task to achieve using sparsely distributed rain gauges. This advantage greatly enhances our understanding of natural precipitation. Also, for this reason, it is difficult to know satellite-derived precipitation system performance using a direct “object-to-object” method involving ground-truth reference data. To solve this issue, we develop a broadly applicable method, namely, a “pixel-to-object” method, which regards discrete gauges as sampling gridboxes to evaluate a satellite precipitation product from the precipitation system perspective. Approximately 46,000 AWSs recorded hourly data in China are used to evaluate the IMERG precipitation product. The results show that the smaller the precipitation systems are, the higher the false alarm proportion, the lower the miss proportion, and the more underestimated the precipitation rates are. The miss and false alarm proportions near the precipitation system boundaries are much larger than those far away from the boundaries, while the precipitation rate bias presents a negative-positive–negative bias pattern from the boundaries to the centers of precipitation systems. These results indicate the strong influences of the precipitation system scale and position in the systems on satellite product biases. A universal overestimation of precipitation system sizes is revealed, which is more severe for small systems than for large systems and in the afternoon than in the morning. The different data sources in the IMERG product throughout a day evidently influence the delineation accuracy of precipitation system size in a day.

Subtypes of dual users of combustible and electronic cigarettes: longitudinal changes in product use and dependence symptomatology.

Abstract: INTRODUCTION Cross-sectional surveys found behavioral heterogeneity among dual users of combustible and electronic cigarettes. Yet, prior classification did not reflect dynamic interactions between cigarette and e-cigarette consumption, which may reveal changes in product-specific dependence. The contexts of dual use that could inform intervention were also understudied. METHODS This study conducted secondary analysis on 13 waves of data from 227 dual users who participated in a 2-year observational study. The k-means method for joint trajectories of cigarette and e-cigarette consumption was adopted to identify the subtypes of dual users. The time-varying effect model was used to characterize the subtype-specific trajectories of cigarette and e-cigarette dependence. The subtypes were also compared in terms of use contexts. RESULTS The four clusters were identified: light dual users, predominant vapers, heavy dual users, and predominant smokers. Although heavy dual users and predominant smokers both smoked heavily at baseline, by maintaining vaping at the weekly to daily level the heavy dual users were able to considerably reduce cigarette use. Yet, the heavy dual users' drop in cigarette dependence was not as dramatic as their drop in cigarette consumption. Predominant vapers appeared to engage in substitution, as they decreased their smoking and increased their e-cigarette dependence. They were also more likely to live in environments with smoking restrictions and report that their use of e-cigarettes reduced cigarette craving and smoking frequency. CONCLUSIONS Environmental constraints can drive substitution behavior and the substitution behavior is able to be sustained if people find the substitute to be effective. IMPLICATIONS This study characterizes subtypes of dual users based on the dynamic interactions between cigarette use and e-cigarette use as well as product-specific trajectories of dependence. The subtypes differ in not only sociodemographic characteristics but also contexts of cigarette and e-cigarette use. Higher motivation to use e-cigarettes to quit smoking and less permissive environment for smoking may promote substitution of cigarettes by e-cigarettes.

Daily Food Insecurity Predicts Lower Positive and Higher Negative Affect: An Ecological Momentary Assessment Study

Abstract: Food insecurity (FI) is a dynamic phenomenon, and its association with daily affect is unknown. We explored the association between daily FI and affect among low-income adults during a 2-seasonal-month period that covered days both pre- and during the COVID-19 pandemic. A total of 29 healthy low-income adults were recruited during fall in 2019 or 2020, 25 of whom were followed in winter in 2020 or 2021. Daily FI (measured once daily) and affect (measured 5 times daily) were collected over the 2nd−4th week in each month. Time-Varying-Effect-Models were used to estimate the association between daily FI and positive/negative affect (PA/NA). Overall, 902 person-days of daily-level data were collected. Daily FI was associated with lower PA in the 3rd and 4th week of fall and winter and with higher NA in the second half of winter months. Similar patterns of FI-affect relations were found pre- and during COVID-19 in the second half of a given month, while unique patterns of positive affect scores in the 2nd week and negative scores in the 1st week were only observed during COVID days. Our study supports a time-varying association between FI and affect in low-income adults. Future large studies are needed to verify the findings; ultimately, better understanding such associations may help identify, target, and intervene in food insecure adults to prevent adverse mental health outcomes.

The situational contexts and subjective effects of co-use of electronic cigarettes and alcohol among college students: An ecological momentary assessment (EMA) study.

Abstract: Understanding the co-use of e-cigarettes and alcohol, including the situational contexts and subjective effects associated with co-use in real-time is necessary for validating this behavior and informing intervention. Yet, the sparse literature has built upon retrospective data.This study recruited 686 college students who were currently using e-cigarettes from three campuses in the Midwest and South of U.S in Fall 2019-Fall 2021. An on-line survey was conducted to measure e-cigarette use patterns, GPA, e-cigarette and alcohol dependence symptoms, and respiratory symptoms. A 7-day ecological momentary assessment was used to collect real-time data on e-cigarette and alcohol use, situational contexts and subjective effects.Frequent drinking e-cigarette users reported more high-risk use behavior including consuming 6 + drinks/occasion and simultaneous use, and reported more e-cigarettes and alcohol related dependence symptoms and respiratory symptoms, compared to infrequent/non-drinker e-cigarette users. Alcohol quantity was positively associated with e-cigarette quantity among the high frequency drinking group. This study identified important use contexts that were associated with higher e-cigarette consumption including use of menthol or fruit flavored e-cigarettes, being in a car, and the presence of others. E-cigarette use and alcohol use both increased the levels of positive affect, physiological sensation, and craving for e-cigarettes, whereas only alcohol use significantly decreased negative affect. No interaction effects between e-cigarette use and alcohol use were found.The findings highlight the addiction and health risks associated with frequent co-use of e-cigarettes and alcohol, and also call for regulations on nontobacco flavorings in e-cigarette products.

Regularized Linear Programming Discriminant Rule with Folded Concave Penalty for Ultrahigh-dimensional Data

Abstract: We propose the regularized linear programming discriminant (LPD) rule with folded concave penalty in the ultrahigh-dimensional regime. We use the local linear approximation (LLA) algorithm to redirect the model with folded concave penalty to a weighted ℓ1 model. The strong oracle property of the solution constructed by the one-step local linear approximation (LLA) algorithm is verified. In addition, we propose efficient and parallelizable algorithms based on feature space split to address the computational challenges due to ultrahigh dimensionality. The proposed feature-split algorithm is compared to existing methods by both numerical simulations and applications to real data examples. The numerical comparisons suggest that the proposed method works well for ultrahigh dimensions, while the linear programming solver and alternating direction method of multiplier (ADMM) algorithm may fail for such high dimensions. Supplementary materials for this article are available online.

Causal discoveries for high dimensional mixed data

Abstract: Causal relationships are of crucial importance for biological and medical research. Algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the normal distribution for all variables, challenges emerge from epidemiological and clinical studies where data are often mixed with continuous, binary, and ordinal variables. We propose to use a mixed latent Gaussian copula model to estimate the underlying correlation structure via the rank correlation for mixed data. This correlation structure is then incorporated into a popular causal discovery algorithm, the PC algorithm, to identify causal structures. The proposed algorithm, called the latent‐PC algorithm, is able to discover the true causal structure consistently under mild conditions in high dimensional settings. From simulation studies, the latent‐PC algorithm delivers a competitive performance in terms of a similar or higher true positive rate and a similar or lower false positive rate, compared with other variants of the PC algorithm. In the high dimensional settings where the number of variables is more than the number of observations, the causal graphs identified by the latent‐PC algorithm are closer to the true causal structures, compared to other competing algorithms. Further, we demonstrate the utility of the latent‐PC algorithm in a real dataset for hepatocellular carcinoma. Causal structures for patient survival are visualized and connected with clinical interpretations in the literature.

High-Dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity

Abstract: Abstract Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a dataset consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of great scientific interest is to identify the active mediating loci out of the 385,882 ones. Houtepen et al. conducted 385,882 linear mediation analyses, in each of which one locus was considered, and identified three active mediating loci. More recently, van Kesteren and Oberski proposed a coordinate-wise mediation filter (CMF) and applied it to the same dataset. They identified five active mediating loci. Unfortunately, the three loci identified by Houtepen et al. are completely different from the five loci identified by van Kesteren and Oberski, probably because both Houtepen et al. and van Kesteren and Oberski did not consider all loci jointly in their analyses. The high dimensional DNA methylation loci indeed necessitate new techniques for identifying active mediating loci and testing the direct and indirect effects of the early life traumatic stress on later cortisol alteration. Motivated by the contradictory results in the aforementioned two scientific works, we develop a new estimating and testing procedure, and apply it to the same dataset as that analyzed by the two works. We identify three new loci: cg19230917, cg06422529 and cg03199124, and their effect sizes and p-values are 321.196 (p-value = 0.035965), 418.173 (p-value = 0.000234) and 471.865 (p-value = 0.001691), respectively. These three loci possess both reasonably neurobiological interpretations and statistically significant effects via our proposed tests. Based on our new procedure, we further confirm that the childhood trauma does not have significant direct effects on cortisol change—it only indirectly affects cortisol through DNA methylation, and the indirect effect is negative. Supplementary materials for this article are available online.

Feature Screening for Interval-Valued Response with Application to Study Association between Posted Salary and Required Skills

Abstract: Abstract It is important to quantify the differences in returns to skills using the online job advertisements data, which have attracted great interest in both labor economics and statistics fields. In this article, we study the relationship between the posted salary and the job requirements in online labor markets. There are two challenges to deal with. First, the posted salary is always presented in an interval-valued form, for example, 5k–10k yuan per month. Simply taking the mid-point or the lower bound as the alternative for salary may result in biased estimators. Second, the number of the potential skill words as predictors generated from the job advertisements by word segmentation is very large and many of them may not contribute to the salary. To this end, we propose a new feature screening method, Absolute Distribution Difference Sure Independence Screening (ADD-SIS), to select important skill words for the interval-valued response. The marginal utility for feature screening is based on the difference of estimated distribution functions via nonparametric maximum likelihood estimation, which sufficiently uses the interval information. It is model-free and robust to outliers. Numerical simulations show that the new method using the interval information is more efficient to select important predictors than the methods only based on the single points of the intervals. In the real data application, we study the text data of job advertisements for data scientists and data analysts in a major China’s online job posting website, and explore the important skill words for the salary. We find that the skill words like optimization, long short-term memory (LSTM), convolutional neural networks (CNN), collaborative filtering, are positively correlated with the salary while the words like Excel, Office, data collection, may negatively contribute to the salary. Supplementary materials for this article are available online.

Testing the effects of high-dimensional covariates via aggregating cumulative covariances

Abstract: In this article, we test for the effects of high-dimensional covariates on the response. In many applications, different components of covariates usually exhibit various levels of variation, which is ubiquitous in high-dimensional data. To simultaneously accommodate such heteroscedasticity and high dimensionality, we propose a novel test based on an aggregation of the marginal cumulative covariances, requiring no prior information on the specific form of regression models. Our proposed test statistic is scale-invariance, tuning-free and convenient to implement. The asymptotic normality of the proposed statistic is established under the null hypothesis. We further study the asymptotic relative efficiency of our proposed test with respect to the state-of-art universal tests in two different settings: one is designed for high-dimensional linear model and the other is introduced in a completely model-free setting. A remarkable finding reveals that, thanks to the scale-invariance property, even under the high-dimensional linear models, our proposed test is asymptotically much more powerful than existing competitors for the covariates with heterogeneous variances while maintaining high efficiency for the homoscedastic ones. Supplementary materials for this article are available online.

Model-Free Statistical Inference on High-Dimensional Data

Abstract: : This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is χ 2 distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed χ 2 tests, which are correlated, to identify important predictors under a model-free framework. To this end, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world data set is used to illustrate the proposed methodology. Abstract Section S.1 consists of some technical lemmas which are used in the proof of Theorem 1, Theorem 2, and Proposition 2. Section S.2 provides the proof of Proposition 2. Section S.3 presents additional simulation results. We first present assumptions on penalty functions used in the penalized least squares in Section 3.

Causal Structural Learning on MPHIA Individual Dataset

Abstract: Abstract The Population-based HIV Impact Assessment (PHIA) is an ongoing project that conducts nationally representative HIV-focused surveys for measuring national and regional progress toward UNAIDS’ 90-90-90 targets, the primary strategy to end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to better understand the key factors that drive the HIV epidemics in the most affected countries in sub-Saharan Africa. In this article, we propose a novel causal structural learning algorithm to discover important covariates and potential causal pathways for 90-90-90 targets. Existing constraint-based causal structural learning algorithms are quite aggressive in edge removal. The proposed algorithm preserves more information about important features and potential causal pathways. It is applied to the Malawi PHIA (MPHIA) dataset and leads to interesting results. For example, it discovers age and condom usage to be important for female HIV awareness; the number of sexual partners to be important for male HIV awareness; and knowing the travel time to HIV care facilities leads to a higher chance of being treated for both females and males. We further compare and validate the proposed algorithm using BIC and using Monte Carlo simulations, and show that the proposed algorithm achieves improvement in true positive rates in important feature discovery over existing algorithms. Supplementary materials for this article are available online.