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Identifying a patient's disease/health status from electronic medical records is a frequently encountered task in electronic health records (EHR) related research, and estimation of a classification model often requires a benchmark training data with patients' known phenotype statuses. However, assessing a patient's phenotype is costly and labor intensive, hence a proper selection of EHR records as a training set is desired. We propose a procedure to tailor the best training subsample with limited sample size for a classification model, minimizing its mean-squared phenotyping/classification error (MSE). Our approach incorporates “positive only” information, an approximation of the true disease status without false alarm, when it is available. In addition, our sampling procedure is applicable for training a chosen classification model which can be misspecified. We provide theoretical justification on its optimality in terms of MSE. The performance gain from our method is illustrated through simulation and a real-data example, and is found often satisfactory under criteria beyond MSE.

 

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.

 

Expression quantitative trait loci (eQTL) studies utilize regression models to explain the variance of gene expressions with genetic loci or single nucleotide polymorphisms (SNPs). However, regression models for eQTL are challenged by the presence of high dimensional non‐sparse and correlated SNPs with small effects, and nonlinear relationships between responses and SNPs. Principal component analyses are commonly conducted for dimension reduction without considering responses. Because of that, this non‐supervised learning method often does not work well when the focus is on discovery of the response‐covariate relationship. We propose a new supervised structural dimensional reduction method for semiparametric regression models with high dimensional and correlated covariates; we extract low‐dimensional latent features from a vast number of correlated SNPs while accounting for their relationships, possibly nonlinear, with gene expressions. Our model identifies important SNPs associated with gene expressions and estimates the association parameters via a likelihood‐based algorithm. A GTEx data application on a cancer related gene is presented with 18 novel eQTLs detected by our method. In addition, extensive simulations show that our method outperforms the other competing methods in bias, efficiency, and computational cost.

 

In an effort to develop and design next generation high power target materials for particle physics research, the possibility of fabricating nonwoven metallic or ceramic nanofibers by electrospinning process is explored. A low-cost electrospinning unit is set up for in-house production of various ceramic nanofibers. Yttria-stabilized zirconia nanofibers are successfully fabricated by electrospinning a mixture of zirconium carbonate with high-molecular weight polyvinylpyrrolidone polymer solution. Some of the inherent weaknesses of electrospinning process like thickness of nanofiber mat and slow production rate are overcome by modifying certain parts of electrospinning system and their arrangements to get thicker nanofiber mats of millimeter order at a faster rate. Continuous long nanofibers of about hundred nanometers in diameter are produced and subsequently heat treated to get rid of polymer and allow crystallize zirconia. Specimens were prepared to meet certain minimum physical properties such as thickness, structural integrity, thermal stability, and flexibility. An easy innovative technique based on atomic force microscopy was employed for evaluating mechanical properties of single nanofiber, which were found to be comparable to bulk zirconia. Nanofibers were tested for their high-temperature resistance using an electron beam. It showed resistance to radiation damage when irradiated with 1 MeV Kr2+ ion. Some zirconia nanofibers were also tested under high-intensity pulsed proton beam and maintained their structural integrity. This study shows for the first time that a ceramic nanofiber has been tested under different beams and irradiation condition to qualify their physical properties for practical use as accelerator targets. Advantages and challenges of such nanofibers as potential future targets over bulk material targets are discussed. 

Abstract Understanding defect evolution and structural transformations constitutes a prominent research frontier for ultimately controlling the electrochemical properties of advanced battery materials. Herein, for the first time, we utilize in situ high-energy Kr ion irradiation with transmission electron microscopy to monitor how defects and microstructures evolve in Na- and Li-layered cathodes with 3d transition metals. Our experimental and theoretical analyses reveal that Li-layered cathodes are more resistant to radiation-induced structural transformations, such as amorphization than Na-layered cathodes. The underlying mechanism is the facile formation of Li-transition metal antisite defects in Li-layered cathodes. The quantitative mathematical analysis of the dynamic bright-field imaging shows that defect clusters preferentially align along the Na/Li ion diffusion channels ( a-b planes), which is likely governed by the formation of dislocation loops. Our study provides critical insights into designing battery materials for extreme irradiation environments and understanding fundamental defect dynamics in layered oxides.