Phase transition of shocked water up to 6 GPa:Transmittance investigation

The phase transition behaviors of the shocked water are investigated by employing an optical transmittance in-situ detection system.Based on the light scattering theory and phase transformation kinetics,the phase transition mechanism of the water under multiple shocks is discussed.The experimental data indicate that the evolution of the transmittance of the shocked water can be broadly divided into three stages:relaxation stage,decline stage,and recovery stage.In the early stage of the phase transition,the new phase particles began to form around the quartz/window interface.It should be mentioned that the water/ice phase boundary seems to move toward the liquid region in one experiment of this work.Due to the new phase core being much smaller than the wavelength of the incident light,the transmittance of the sample within the relaxation stage remains steady.The decline stage can be divided into the rapid descent stage and the slow descent stage in this work,which is considered as the different growth rates of the new phase particle under different shock loadings.The recovery stage is attributed to the emergence of the new phase particles which are bigger than the critical value.However,the influence of the size growth and the population growth of the new phase particles on the transmittance restrict each other,which may be responsible for the phenomenon that the transmittance curve does not return to the initial level.

Weighted Least Squares Method for the Accelerated Failure Time Model with Auxiliary Covariates

This paper deals with the analysis of accelerated failure time model when the primary covariate is subject to missing. We assume that the true covariate is measured precisely on a randomly chosen validation set, whereas auxiliary information for primary covariate is available to all study subjects. The asymptotic properties for the proposed estimator are developed and the simulation studies show that the efficiency gain is remarkable compared to the method using only the validation sample. A real example is also provided as an illustration.

Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative method.Methods:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy.In the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best performance.Using an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual scores.Furthermore,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.Results:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family history.The overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family history.Conclusions:We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa.Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.