Multi-ancestry Genome-Wide Association Study of Early Childhood Caries.

Early childhood caries (ECC) is the most common non-communicable childhood disease. It is an important health problem with known environmental and social/behavioral influences that lacks evidence for specific associated genetic risk loci. To address this knowledge gap, we conducted a genome-wide association study of ECC in a multi-ancestry population of U.S. preschool-age children (n=6,103) participating in a community-based epidemiologic study of early childhood oral health. Calibrated examiners used ICDAS criteria to measure ECC with the primary trait using the dmfs index with decay classified as macroscopic enamel loss (ICDAS ≥3). We estimated heritability, concordance rates, and conducted genome-wide association analyses to estimate overall genetic effects; the effects stratified by sex, household water fluoride, and dietary sugar; and leveraged the combined gene/gene-environment effects using the 2-degree-of-freedom (2df) joint test. The common genetic variants explained 24% of the phenotypic variance (heritability) of the primary ECC trait and the concordance rate was higher with a higher degree of relatedness. We identified 21 novel non-overlapping genome-wide significant loci for ECC. Two loci, namely RP11-856F16 . 2 (rs74606067) and SLC41A3 (rs71327750) showed evidence of association with dental caries in external cohorts, namely the GLIDE consortium adult cohort (n=∼487,000) and the GLIDE pediatric cohort (n=19,000), respectively. The gene-based tests identified TAAR6 as a genome-wide significant gene. Implicated genes have relevant biological functions including roles in tooth development and taste. These novel associations expand the genomics knowledge base for this common childhood disease and underscore the importance of accounting for sex and pertinent environmental exposures in genetic investigations of oral health.

Marginal proportional hazards models for multivariate interval-censored data.

Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.

An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.

High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been proposed to solve the problem of high-dimensional classification. However, these methods rely on inverting a large matrix or solving large-scale optimization problems to render classification rules-methods that are computationally prohibitive when the dimension is ultra-high. With the emergence of big data, it is increasingly important to develop more efficient algorithms to solve the high-dimensional LDA problem. In this paper, we propose an efficient greedy search algorithm that depends solely on closed-form formulae to learn a high-dimensional LDA rule. We establish theoretical guarantee of its statistical properties in terms of variable selection and error rate consistency; in addition, we provide an explicit interpretation of the extra information brought by an additional feature in a LDA problem under some mild distributional assumptions. We demonstrate that this new algorithm drastically improves computational speed compared with other high-dimensional LDA methods, while maintaining comparable or even better classification performance.

PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA.

Recent technological advances have made it possible to measure multiple types of many features in biomedical studies. However, some data types or features may not be measured for all study subjects because of cost or other constraints. We use a latent variable model to characterize the relationships across and within data types and to infer missing values from observed data. We develop a penalized-likelihood approach for variable selection and parameter estimation and devise an efficient expectation-maximization algorithm to implement our approach. We establish the asymptotic properties of the proposed estimators when the number of features increases at a polynomial rate of the sample size. Finally, we demonstrate the usefulness of the proposed methods using extensive simulation studies and provide an application to a motivating multi-platform genomics study.

Semiparametric single-index models for optimal treatment regimens with censored outcomes.

There is a growing interest in precision medicine, where a potentially censored survival time is often the most important outcome of interest. To discover optimal treatment regimens for such an outcome, we propose a semiparametric proportional hazards model by incorporating the interaction between treatment and a single index of covariates through an unknown monotone link function. This model is flexible enough to allow non-linear treatment-covariate interactions and yet provides a clinically interpretable linear rule for treatment decision. We propose a sieve maximum likelihood estimation approach, under which the baseline hazard function is estimated nonparametrically and the unknown link function is estimated via monotone quadratic B-splines. We show that the resulting estimators are consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. The optimal treatment rule follows naturally as a linear combination of the maximum likelihood estimators of the model parameters. Through extensive simulation studies and an application to an AIDS clinical trial, we demonstrate that the treatment rule derived from the single-index model outperforms the treatment rule under the standard Cox proportional hazards model.

SEMIPARAMETRIC LATENT-CLASS MODELS FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.

In long-term follow-up studies, data are often collected on repeated measures of multivariate response variables as well as on time to the occurrence of a certain event. To jointly analyze such longitudinal data and survival time, we propose a general class of semiparametric latent-class models that accommodates a heterogeneous study population with flexible dependence structures between the longitudinal and survival outcomes. We combine nonparametric maximum likelihood estimation with sieve estimation and devise an efficient EM algorithm to implement the proposed approach. We establish the asymptotic properties of the proposed estimators through novel use of modern empirical process theory, sieve estimation theory, and semiparametric efficiency theory. Finally, we demonstrate the advantages of the proposed methods through extensive simulation studies and provide an application to the Atherosclerosis Risk in Communities study.

Maximum Likelihood Estimation for Semiparametric Regression Models With Panel Count Data.

Panel count data, in which the observation for each study subject consists of the number of recurrent events between successive examinations, are commonly encountered in industrial reliability testing, medical research, and various other scientific investigations. We formulate the effects of potentially time-dependent covariates on one or more types of recurrent events through non-homogeneous Poisson processes with random effects. We adopt nonparametric maximum likelihood estimation under arbitrary examination schemes and develop a simple and stable EM algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that achieves the semiparametric efficiency bound and can be estimated through profile likelihood. We evaluate the performance of the proposed methods through extensive simulation studies and present a skin cancer clinical trial.

Ultrahigh-strength and ductile superlattice alloys with nanoscale disordered interfaces.

Alloys that have high strengths at high temperatures are crucial for a variety of important industries including aerospace. Alloys with ordered superlattice structures are attractive for this purpose but generally suffer from poor ductility and rapid grain coarsening. We discovered that nanoscale disordered interfaces can effectively overcome these problems. Interfacial disordering is driven by multielement cosegregation that creates a distinctive nanolayer between adjacent micrometer-scale superlattice grains. This nanolayer acts as a sustainable ductilizing source, which prevents brittle intergranular fractures by enhancing dislocation mobilities. Our superlattice materials have ultrahigh strengths of 1.6 gigapascals with tensile ductilities of 25% at ambient temperature. Simultaneously, we achieved negligible grain coarsening with exceptional softening resistance at elevated temperatures. Designing similar nanolayers may open a pathway for further optimization of alloy properties.

Silica-based colloid centrifugation enhances sperm quality in cockerel semen.

1. PercollTM is one of the most widely used colloid for animal sperm preparation. The aim of this study was to evaluate whether PercollTM colloid centrifugation could be practical to improve cockerel sperm quality, and to compare the effects of PercollTM single layer centrifugation (SLC) and density gradient centrifugation (DGC) in order to obtain the most optimal protocol for cockerel semen.2. In the experiment with PercollTM SLC for fresh semen, an increase of motile sperm was seen after PercollTM 80% SLC and 90% SLC was conducted, at levels of 28.8% and 30.2% respectively (P < 0.01). The increase of progressively motile sperm after PercollTM 80% SLC and 90% SLC was 177.2% and 202.4% respectively (P < 0.01). Meanwhile, for semen stored at 4°C for 24 h, the increase of motile sperm after PercollTM 70% SLC and 80% SLC was 41.2% and 44.0% (P < 0.01), and the increase of progressive sperm after PercollTM 70% SLC and 80% SLC was 71.3% and 83.1% respectively (P < 0.01). Both the percentage of motile sperm and progressive sperm of the fresh and stored cockerel semen after appropriate PercollTM SLC was significantly enhanced.3. Sperm membrane integrity did not show any decrease after PercollTM centrifugation compared with non-centrifuged semen, which suggested that the PercollTM centrifugation treatment in this study did not cause damage to cockerel sperm membranes.4. In the experiment regarding the comparison of PercollTM SLC and DGC with fresh semen, the increase of motile sperm after PercollTM 80% SLC, 90% SLC and 40%/80% DGC was 29.5%, 36.4%, and 25.0% respectively; and the increase of progressive sperm was 44.7%, 58.5%, and 54.7%, respectively. For semen stored at 4°C for 24 h, the increase of motile sperm after PercollTM 70% SLC, 80% SLC and 35%/70% DGC were 41.2%, 44.0%, and 26.4%; and the increase of progressive sperm was 71.3%, 83.1%, and 43.7%, respectively. There were no significant differences between the increase of sperm motility after PercollTM 80%, 90% SLC or PercollTM 40%/80% DGC in fresh cockerel semen. There was no significant difference between PercollTM 70%, 80% SLC and PercollTM 35%/70% in stored cockerel semen. There was a tendency for sperm recovery rates with PercollTM SLC to be higher than PercollTM DGC, although this did not reach statistical significance in this study.5. It was concluded that PercollTM SLC was more suitable for cockerel sperm separation than PercollTM DGC. The results suggested that PercollTM 80% SLC was the most optimal procedure to separate fresh cockerel sperm and PercollTM 70% SLC was the most optimal procedure to separate stored cockerel sperm. PercollTM SLC is more simple, user-friendly and economical and less time-consuming than DGC for cockerel semen processing.

Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses.

The pharmaceutical industry and regulatory agencies are increasingly interested in conducting bridging studies in order to bring an approved drug product from the original region (eg, United States or European Union) to a new region (eg, Asian-Pacific countries). In this article, we provide a new methodology for the design and analysis of bridging studies by assuming prior knowledge on how the null and alternative hypotheses in the original, foreign study are related to the null and alternative hypotheses in the bridging study and setting the type I error for the bridging study according to the strength of the foreign-study evidence. The new methodology accounts for randomness in the foreign-study evidence and controls the average type I error of the bridging study over all possibilities of the foreign-study evidence. In addition, the new methodology increases statistical power, when compared to approaches that do not use foreign-study evidence, and it allows for the possibility of not conducting the bridging study when the foreign-study evidence is unfavorable. Finally, we conducted extensive simulation studies to demonstrate the usefulness of the proposed methodology.

Semiparametric Regression Analysis of Multiple Right- and Interval-Censored Events.

Health sciences research often involves both right- and interval-censored events because the occurrence of a symptomatic disease can only be observed up to the end of follow-up, while the occurrence of an asymptomatic disease can only be detected through periodic examinations. We formulate the effects of potentially time-dependent covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects that capture the dependence both within and between the two types of events. We consider nonparametric maximum likelihood estimation and develop a simple and stable EM algorithm for computation. We show that the resulting estimators are consistent and the parametric components are asymptotically normal and efficient with a covariance matrix that can be consistently estimated by profile likelihood or nonparametric bootstrap. In addition, we leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history. Furthermore, we assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an application to a major epidemiological cohort study. Supplementary materials for this article are available online.

[Validation and optimization of the indicator system of risk assessment for mechanical cuts].

Objective: To validation and optimization the indicator system of risk assessment for mechanical cuts. Methods: The risk assessment index system of mechanical cutting injury established earlier was used to assess the risk of mechanical cutting injury in 40 cases of mechanical cutting injury registered from January 2015 to December 2017 and 40 similar positions without accidents in the same period. The multiple stepwise regression analysis was used to screen the indicator system, and to adjust the weight coefficient of each index. The total coincidence rate and Kappa value were compared between before and after optimization respectively. Results: The new index system has 3 first-class indicators, 10 second-class indicators and 14 three-class indicators, fewer than the old index system which has 3 first-class indicators, 10 second-class indicators, 34 three-class indicators. There three indicators have revamped in the first-class. The total of coincidence rates of the new and old indicator systems were 67.50% and 90.00%, the difference was statistically significant (P<0.01). The Kappa value were 0.35 and 0.80, respectively. Conclusion: The evaluation results with new indicator systems is more consistent with the actual hazard detection the the old indicator systems, and scientific, reasonable and practical, and the indicator system of risk assessment for mechanical cuts can be used for the risk assessment of mechanical cutting injuries.

Evaluation of cockerel spermatozoa viability and motility by a novel enzyme based cell viability assay.

1. The results of spermatozoa assessment by the WST-8 (2-[2-methoxy-4-nitrophenyl]-3-[4-nitrophenyl]-5-[2,4-disulfophenyl]-2H-tetrazolium, monosodium salt) assay, flow cytometry (FC) or computer-assisted sperm analysis (CASA) were compared. 2. Different live/killed ratios of cockerel semen were serially diluted to 120, 60, and 30 × 106 cells/ml, and each sample was analysed by (1) WST-8 assay at 0, 10, 20, 30, 40, 50, 60 min, (2) viability with FC, and (3) motility with CASA. 3. The WST-8 reduction rate was closely correlated with spermatozoa viability and motility. The optimal semen concentration for the WST-8 assay was 120 × 106 cells/ml, and the standard curves for spermatozoa viability and motility predictions, respectively, were yviability60 = 162.8x + 104.96 (R2 = 0.9594) after 60 min of incubation and ymotility40 = 225.09x + 96.299 (R2 = 0.8475) after 40 min of incubation. 4. It was concluded that the WST-8 assay is useful for the practical evaluation of cockerel spermatozoa viability and motility. Compared to FC and CASA, the WST-8 assay does not require expensive and complex instrumentation in the lab. Furthermore, one well of the WST-8 reaction can be used to predict spermatozoa viability and motility at the same time, which all lead it to be efficient and economical for semen quality assessment.

Robust Score Tests With Missing Data in Genomics Studies.

Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this paper, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.

Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.

Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined Expectation-Maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, we demonstrate the satisfactory performance of the proposed methods through simulation studies and provide an application to a motivating cancer study that contains a variety of genomic variables. Supplementary materials for this article are available online.

Trans-ethnic analysis of metabochip data identifies two new loci associated with BMI.

OBJECTIVE: Body mass index (BMI) is commonly used to assess obesity, which is associated with numerous diseases and negative health outcomes. BMI has been shown to be a heritable, polygenic trait, with close to 100 loci previously identified and replicated in multiple populations. We aim to replicate known BMI loci and identify novel associations in a trans-ethnic study population. SUBJECTS: Using eligible participants from the Population Architecture using Genomics and Epidemiology consortium, we conducted a trans-ethnic meta-analysis of 102 514 African Americans, Hispanics, Asian/Native Hawaiian, Native Americans and European Americans. Participants were genotyped on over 200 000 SNPs on the Illumina Metabochip custom array, or imputed into the 1000 Genomes Project (Phase I). Linear regression of the natural log of BMI, adjusting for age, sex, study site (if applicable), and ancestry principal components, was conducted for each race/ethnicity within each study cohort. Race/ethnicity-specific, and combined meta-analyses used fixed-effects models. RESULTS: We replicated 15 of 21 BMI loci included on the Metabochip, and identified two novel BMI loci at 1q41 (rs2820436) and 2q31.1 (rs10930502) at the Metabochip-wide significance threshold (P<2.5 × 10-7). Bioinformatic functional investigation of SNPs at these loci suggests a possible impact on pathways that regulate metabolism and adipose tissue. CONCLUSION: Conducting studies in genetically diverse populations continues to be a valuable strategy for replicating known loci and uncovering novel BMI associations.

Improving the power to establish clinical similarity in a Phase 3 efficacy trial by incorporating prior evidence of analytical and pharmacokinetic similarity.

To improve patients' access to safe and effective biological medicines, abbreviated licensure pathways for biosimilar and interchangeable biological products have been established in the US, Europe, and other countries around the world. The US Food and Drug Administration and European Medicines Agency have published various guidance documents on the development and approval of biosimilars, which recommend a "totality-of-the-evidence" approach with a stepwise process to demonstrate biosimilarity. The approach relies on comprehensive comparability studies ranging from analytical and nonclinical studies to clinical pharmacokinetic/pharmacodynamic (PK/PD) and efficacy studies. A clinical efficacy study may be necessary to address residual uncertainty about the biosimilarity of the proposed product to the reference product and support a demonstration that there are no clinically meaningful differences. In this article, we propose a statistical strategy that takes into account the similarity evidence from analytical assessments and PK studies in the design and analysis of the clinical efficacy study in order to address residual uncertainty and enhance statistical power and precision. We assume that if the proposed biosimilar product and the reference product are shown to be highly similar with respect to the analytical and PK parameters, then they should also be similar with respect to the efficacy parameters. We show that the proposed methods provide correct control of the type I error and improve the power and precision of the efficacy study upon the standard analysis that disregards the prior evidence. We confirm and illustrate the theoretical results through simulation studies based on the biosimilars development experience of many different products.

[The value of (18)F-FDG PET-CT imaging in predicting the malignant potential of GIST].

Objective: To evaluate the value of (18)F-FDG PET-CT in predicting the malignant potential of Gastrointestinal Stromal Tumors (GIST). Methods: The clinical and pathological features of 31 patients with GIST confirmed by surgery or biopsy were retrospectively analyzed. The malignant potential of GIST before treatment was assessed by (18)F-FDG PET-CT. The GIST risk classification was graded according to the Standard revised by the National Institutes of Health (NIH) in 2008. The relationship between the maximal standard uptake value (SUVmax) and GIST risk classification, tumor diameter, Ki-67 index, and mitotic count were analyzed respectively. The cut-off level of SUVmax for the diagnosis of malignant GIST was calculated from the Receiver Operating Characteristic (ROC) curve. Results: Among the 31 cases of GIST patients, 14 cases were gastric primary (stomach group) and 17 cases were nongastric primary (outside stomach group). The SUVmax, tumor diameter, Ki-67 index and mitotic count of the 31 patients were 8.21±4.68, (7.82±5.12)cm, (10.03±11.07)% and (12.29±10.55)/50 HPF, respectively. SUVmax was significantly correlated with GIST risk classification (r=0.727, P<0.01), but not with tumor diameter, Ki-67 index and mitotic count (r=0.348, r=0.284, r=0.290, P=0.055, P=0.121, P=0.114). The SUVmax, tumor diameter, Ki-67 index and mitotic count in the stomach group were 4.36±2.36, (6.08±4.31)cm, (3.43±3.03)% and (5.71±2.20)/50 HPF, respectively. SUVmax was significantly correlated with tumor diameter, GIST risk classification and Ki-67 index (r=0.682, r=0.868, r=0.732, P<0.01) but not with mitotic count (r=0.510, P=0.063). The SUVmax of the GIST in the gastric group and the outside gastric group were 4.36±2.36 and 10.68±5.50, respectively. The difference was statistically significant (P=0.001). The SUVmax in the malignant group of GIST (middle or high risk grade) was 8.90±4.89, which was significantly higher than 2.22±0.86 in the benign group (low or very low risk grade). The difference was statistically significant between the two group (P<0.01). ROC curve analysis showed that a SUVmax cut-off of 3.75 was the most sensitive for predicting malignant GIST. When the area under the curve of 0.969, the sensitivity was 84.6% and the specificity was 100%. Conclusions: The SUVmax was strongly correlated with the GIST risk category and also with the tumor diameter and Ki-67 index in the gastric primary GIST, so it can be used as an effective indicator in predicting malignant potential of GIST before treatment.