Supplemental material for Associations between genetic polymorphisms of TLRs and susceptibility to tuberculosis: A meta-analysis

Supplemental Material for Associations between genetic polymorphisms of TLRs and susceptibility to tuberculosis: A meta-analysis by Yong Zhou and Mengtao Zhang in Innate Immunity</p

Data_Sheet_1_Meta-Analysis of Vitamin D Receptor Gene Polymorphisms in Childhood Asthma.docx

We conducted the systematic review to investigate the potential relationship between the vitamin polymorphisms of D receptor (VDR) gene and childhood asthma. Relevant studies researching on VDR polymorphisms and asthma susceptibility were searched throughout Embase, PubMed, China Science and technology journal database (CQVIP), etc. till 12 April, 2021. We calculated the pooled odds ratios (OR) and its 95% confidence interval (CI) using RevMan 5.3 software and Stata 11.0. FokI (rs2228570) could significantly affect childhood asthma risk across co dominant model (Ff vs. FF: OR (95%CI) = 0.82 (0.65, 1.02), P = 0.071) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.77 (0.63, 0.95), P = 0.016), especially among Caucasians in additive model (f vs. F: OR (95%CI) = 0.63 (0.43, 0.92), P = 0.015) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.67 (0.51, 0.88), P = 0.004). TaqI (rs731236) was significantly related with childhood asthma in additive model (t vs. T: OR (95%CI) = 0.45 (0.23, 0.89), P = 0.022), co dominant model (Tt vs. TT: OR (95%CI) = 0.36 (0.17, 0.77), P = 0.009), and dominant model (tt+Tt vs. TT: OR (95%CI) = 0.36 (0.15, 0.87), P = 0.024) among Asian, as well as population-based subgroup in co dominant model (Tt vs. TT: OR (95%CI) = 0.53 (0.31, 0.94), P = 0.029). However, no evidence supported the role of ApaI (rs7975232) and BsmI (rs1544410) polymorphisms in childhood asthma. FokI and TaqI polymorphisms were found to be related with the susceptibility of childhood asthma. However, it seems that ApaI and BsmI polymorphisms are not related with childhood asthma susceptibility.</p

Synthesis of Very Small TiO₂ Nanocrystals in a Room-Temperature Ionic Liquid and Their Self-Assembly toward Mesoporous Spherical Aggregates

In this Communication, we describe the synthesis of 2−3 nm sized titania nanocrystals in a room-temperature ionic liquid under mild conditions and their self-assembly toward mesoporous TiO2 spheres. The resulting structures combine the convenient handling of larger spheres with a considerable high surface area and narrow pore size distribution and are expected to have potential in solar energy conversion, catalysis, and optoelectronic devices

Semiparametric additive frailty hazard model for clustered failure time data

This article proposes a flexible semiparametric additive frailty hazard model under clustered failure time data, where frailty is assumed to have an additive effect on the hazard function. When there is no frailty, this model degenerates into a semiparametric additive hazard model. Our method can deal simultaneously with both time‐varying and constant covariate effects. The estimate of the covariate effects does not rely on the frailty distribution. The time‐varying coefficient is estimated by utilizing the local linear technique, while we can obtain a ‐consistency convergence rate of the constant‐coefficient estimate by integration. Another advantage of the estimator is that it has a closed form. We establish large sample properties of the estimator and conduct simulation studies under various scenarios to demonstrate its performance. The proposed method is applied to real data for illustration.</p

A General M-estimation Theory in Semi-Supervised Framework

We study a class of general M-estimators in the semi-supervised setting, wherein the data are typically a combination of a relatively small labeled dataset and large amounts of unlabeled data. A new estimator, which efficiently uses the useful information contained in the unlabeled data, is proposed via a projection technique. We prove consistency and asymptotic normality, and provide an inference procedure based on K-fold cross-validation. The optimal weights are derived to balance the contributions of the labeled and unlabeled data. It is shown that the proposed method, by taking advantage of the unlabeled data, produces asymptotically more efficient estimation of the target parameters than the supervised counterpart. Supportive numerical evidence is shown in simulation studies. Applications are illustrated in analysis of the homeless data in Los Angeles. Supplementary materials for this article are available online.</p

Table1_Path analysis method in an epidemic model and stability analysis.docx

In this paper, a new method for obtaining the basic reproduction number is proposed, called the path analysis method. Compared with the traditional next-generation method, this method is more convenient and less error-prone. We develop a general model that includes most of the epidemiological characteristics and enumerate all disease transmission paths. The path analysis method is derived by combining the next-generation method and the disease transmission paths. Three typical examples verify the effectiveness and convenience of the method. It is important to note that the path analysis method is only applicable to epidemic models with bilinear incidence rates. The Volterra-type Lyapunov function is given to prove the global stability of the system. The simulations prove the correctness of our conclusions.</p

From Conditional Quantile Regression to Marginal Quantile Estimation with Applications to Missing Data and Causal Inference

It is well known that information on the conditional distribution of an outcome variable given covariates can be used to obtain an enhanced estimate of the marginal outcome distribution. This can be done easily by integrating out the marginal covariate distribution from the conditional outcome distribution. However, to date, no analogy has been established between marginal quantile and conditional quantile regression. This article provides a link between them. We propose two novel marginal quantile and marginal mean estimation approaches through conditional quantile regression when some of the outcomes are missing at random. The first of these approaches is free from the need to choose a propensity score. The second is double robust to model misspecification: it is consistent if either the conditional quantile regression model is correctly specified or the missing mechanism of outcome is correctly specified. Consistency and asymptotic normality of the two estimators are established, and the second double robust estimator achieves the semiparametric efficiency bound. Extensive simulation studies are performed to demonstrate the utility of the proposed approaches. An application to causal inference is introduced. For illustration, we apply the proposed methods to a job training program dataset.</p

Variable screening for ultrahigh dimensional censored quantile regression

Quantile regression is a flexible approach to assessing covariate effects on failure time, which has attracted considerable interest in survival analysis. When the dimension of covariates is much larger than the sample size, feature screening and variable selection become extremely important and indispensable. In this article, we introduce a new feature screening method for ultrahigh dimensional censored quantile regression. The proposed method can work for a general class of survival models, allow for heterogeneity of data and enjoy desirable properties including the sure screening property and the ranking consistency property. Moreover, an iterative version of screening algorithm has also been proposed to accommodate more complex situations. Monte Carlo simulation studies are designed to evaluate the finite sample performance under different model settings. We also illustrate the proposed methods through an empirical analysis.</p

Robust scheduling for multi-objective flexible job-shop problems with flexible workdays

<p>This study determines a robust schedule for a flexible job-shop scheduling problem with flexible workdays. The performance criteria considered in this study are tardiness, overtime and robustness. Furthermore, the problem is addressed in a Pareto manner, and a set of Pareto-optimal solutions is determined for this purpose. In consideration of all the aforementioned features, a goal-guided neighbourhood function is proposed based on efficient problem-dependent move-filtering methods. Two metaheuristic algorithms, named goal-guided multi-objective tabu search and goal-guided multi-objective hybrid search, are proposed in this work based on this neighbourhood function. The effectiveness of these approaches is demonstrated via empirical studies.</p

Significant mathematical equations and regression coefficients (a, b, c, d, e, f) used to predict structure and composition of Gmelin larch forests from linear and quadratic components of Isothermality, TempWarmestMonth, PrecipWettestMonth, PrecipSeasonality, PrecipDriestQuarter and Max.DBH_Larch.

<p>Equations from stepwise regression analyses (n = 12). S.E., standard error; , adjusted multiple coefficient of determination. Significance level:^NS Non-significant (P>0.05), *P<0.05, **P<0.01, ***P<0.001.</p