MSH7 confers quantitative variation in pollen fertility and boosts grain yield in maize.

Fertile pollen is critical for the survival, fitness, and dispersal of flowering plants, and directly contributes to crop productivity. Extensive mutational screening studies have been carried out to dissect the genetic regulatory network determining pollen fertility, but we still lack fundamental knowledge about whether and how pollen fertility is controlled in natural populations. We used a genome-wide association study (GWAS) to show that ZmGEN1A and ZmMSH7, two DNA repair-related genes, confer natural variation in maize pollen fertility. Mutants defective in these genes exhibited abnormalities in meiotic or post-meiotic DNA repair, leading to reduced pollen fertility. More importantly, ZmMSH7 showed evidence of selection during maize domestication, and its disruption resulted in a substantial increase in grain yield for both inbred and hybrid. Overall, our study describes the first systematic examination of natural genetic effects on pollen fertility in plants, providing valuable genetic resources for optimizing male fertility. In addition, we find that ZmMSH7 represents a candidate for improvement of grain yield.

Soybean steroids improve crop abiotic stress tolerance and increase yield.

Sterols have long been associated with diverse fields, such as cancer treatment, drug development, and plant growth; however, their underlying mechanisms and functions remain enigmatic. Here, we unveil a critical role played by a GmNF-YC9-mediated CCAAT-box transcription complex in modulating the steroid metabolism pathway within soybeans. Specifically, this complex directly activates squalene monooxygenase (GmSQE1), which is a rate-limiting enzyme in steroid synthesis. Our findings demonstrate that overexpression of either GmNF-YC9 or GmSQE1 significantly enhances soybean stress tolerance, while the inhibition of SQE weakens this tolerance. Field experiments conducted over two seasons further reveal increased yields per plant in both GmNF-YC9 and GmSQE1 overexpressing plants under drought stress conditions. This enhanced stress tolerance is attributed to the reduction of abiotic stress-induced cell oxidative damage. Transcriptome and metabolome analyses shed light on the upregulation of multiple sterol compounds, including fucosterol and soyasaponin II, in GmNF-YC9 and GmSQE1 overexpressing soybean plants under stress conditions. Intriguingly, the application of soybean steroids, including fucosterol and soyasaponin II, significantly improves drought tolerance in soybean, wheat, foxtail millet, and maize. These findings underscore the pivotal role of soybean steroids in countering oxidative stress in plants and offer a new research strategy for enhancing crop stress tolerance and quality from gene regulation to chemical intervention.

Order selection for heterogeneous semiparametric hidden Markov models.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

The pattern of cytokines expression and dynamic changes of renal function at 6 months in patients with Omicron COVID-19.

To analyze the dynamic changes of renal function longitudinally and investigate the cytokine profiles at 6 months in patients with Omicron COVID-19. Forty-seven patients with a proven diagnosis of Omicron COVID-19 from January to February 2022 attended a 6-month follow-up after discharge at Tianjin First Central Hospital. The demographic parameters, clinical features, and laboratory indexes were collected during hospitalization and 6 months after discharge. The serum cytokine levels at 6 months were also assessed. Patients were grouped according to with or without kidney involvement at admission. The levels of serum creatinine and estimated glomerular filtration rate (eGFR) were all normal both in the hospital and at follow-up. Whereas, compared with renal function in the hospital, serum creatinine levels at 6 months increased remarkably; meanwhile, eGFR decreased significantly in all patients. The serum levels of interleukin (IL)-2, IL-4, IL-5, IL-6, IL-10, and TNF-α and IFN-γ significantly decreased and TGF-ß remarkably increased in the kidney involvement group. The serum levels of IL-2 and IL-5 were positively correlated with age; contrarily, TGF-ß showed a negative correlation with aging. The younger was an independent risk factor of the higher TGF-ß levels. Omicron patients showed a decline in renal function at follow-up, reflecting the trend of CKD. Serum cytokine profiles were characterized with the majority of cytokines decreased and TGF-ß increased in the kidney involvement group; the latter may be used as a sign of CKD. The tendency of CKD is one of the manifestations of long COVID and deserves attention.

Co-expression of four penaeidins in transgenic rice seeds: an alternative strategy for substitute antibiotic agricultural products.

The co-expression of multiple antimicrobial peptides (AMPs) in genetically modified (GM) crops can give plants a broader antibacterial spectrum and lower the pathogen risk of drug resistance. Therefore, four penaeidins (shrimp-derived AMPs) were fused and encoded in an artificial gene (PEN1234), driven by the seed-specific promoter Pzein, with the aim of co-expression in seeds of transgenic rice. The resistant rice plants, acquired via Agrobacterium-mediated transformation and glufosinate screening, were identified by PCR and the modified disk-diffusion method, and eight GM lines with high AMP content in the seeds were obtained. Among them, the PenOs017 line had the largest penaeidin content, at approximately 251-300 µg/g in seeds and 15-47 µg/g in roots and leaves. The AMPs in the seeds kept their antibacterial properties even after the seed had been boiled in hot water and could significantly inhibit the growth of methicillin-resistant Staphylococcus aureus, and AMPs in the leaves could effectively inhibit Xanthomonas oryzae pv. Oryzae. The results indicate that PenOs017 seeds containing AMPs are an ideal raw-material candidate for antibiotic-free food and feed, and may require fewer petrochemical fungicides or bactericides for disease control during cultivation than conventional rice.

Hyper-expression of GFP-fused active hFGF21 in tobacco chloroplasts.

Human fibroblast growth factor 21 (hFGF21) is a promising candidate for metabolic diseases. In this study, a tobacco chloroplast transformation vector, pWYP21406, was constructed that consisted of codon-optimized encoding gene hFGF21 fused with GFP at its 5' terminal; it was driven by the promoter of plastid rRNA operon (Prrn) and terminated by the terminator of plastid rps16 gene (Trps16). Spectinomycin-resistant gene (aadA) was the marker and placed in the same cistron between hFGF21 and the terminator Trps16. Transplastomic plants were generated by the biolistic bombardment method and proven to be homoplastic by Southern blotting analysis. The expression of GFP was detected under ultraviolet light and a laser confocal microscope. The expression of GFP-hFGF21 was confirmed by immunoblotting and quantified by enzyme-linked immunosorbnent assay (ELISA). The accumulation of GFP-hFGF21 was confirmed to be 12.44 ± 0.45% of the total soluble protein (i.e., 1.9232 ± 0.0673 g kg-1 of fresh weight). GFP-hFGF21 promoted the proliferation of hepatoma cell line HepG2, inducing the expression of glucose transporter 1 in hepatoma HepG2 cells and improving glucose uptake. These results suggested that a chloroplast expression is a promising approach for the production of bioactive recombinant hFGF21.

Feature screening with latent responses.

A novel feature screening method is proposed to examine the correlation between latent responses and potential predictors in ultrahigh-dimensional data analysis. First, a confirmatory factor analysis (CFA) model is used to characterize latent responses through multiple observed variables. The expectation-maximization algorithm is employed to estimate the parameters in the CFA model. Second, R-Vector (RV) correlation is used to measure the dependence between the multivariate latent responses and covariates of interest. Third, a feature screening procedure is proposed on the basis of an unbiased estimator of the RV coefficient. The sure screening property of the proposed screening procedure is established under certain mild conditions. Monte Carlo simulations are conducted to assess the finite-sample performance of the feature screening procedure. The proposed method is applied to an investigation of the relationship between psychological well-being and the human genome.

Semiparametric probit regression model with misclassified current status data.

Current status data arise when each subject under study is examined only once at an observation time, and one only knows the failure status of the event of interest at the observation time rather than the exact failure time. Moreover, the obtained failure status is frequently subject to misclassification due to imperfect tests, yielding misclassified current status data. This article conducts regression analysis of such data with the semiparametric probit model, which serves as an important alternative to existing semiparametric models and has recently received considerable attention in failure time data analysis. We consider the nonparametric maximum likelihood estimation and develop an expectation-maximization (EM) algorithm by incorporating the generalized pool-adjacent-violators (PAV) algorithm to maximize the intractable likelihood function. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semiparametrically efficient. Furthermore, the numerical results in simulation studies indicate that the proposed method performs satisfactorily in finite samples and outperforms the naive method that ignores misclassification. We then apply the proposed method to a real dataset on chlamydia infection.

Simultaneous variable selection in regression analysis of multivariate interval-censored data.

Multivariate interval-censored data arise when each subject under study can potentially experience multiple events and the onset time of each event is not observed exactly but is known to lie in a certain time interval formed by adjacent examination times with changed statuses of the event. This type of incomplete and complex data structure poses a substantial challenge in practical data analysis. In addition, many potential risk factors exist in numerous studies. Thus, conducting variable selection for event-specific covariates simultaneously becomes useful in identifying important variables and assessing their effects on the events of interest. In this paper, we develop a variable selection technique for multivariate interval-censored data under a general class of semiparametric transformation frailty models. The minimum information criterion (MIC) method is embedded in the optimization step of the proposed expectation-maximization (EM) algorithm to obtain the parameter estimator. The proposed EM algorithm greatly reduces the computational burden in maximizing the observed likelihood function, and the MIC naturally avoids selecting the optimal tuning parameter as needed in many other popular penalties, making the proposed algorithm promising and reliable. The proposed method is evaluated through extensive simulation studies and illustrated by an analysis of patient data from the Aerobics Center Longitudinal Study.

Bayesian transformation models with partly interval-censored data.

In many scientific fields, partly interval-censored data, which consist of exactly observed and interval-censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval-censored data are relatively limited and have mainly focused on additive or proportional hazards models. The general linear transformation model provides a highly flexible modeling framework that includes several familiar survival models as special cases. Despite such nice features, the inference procedure for this class of models has not been developed for partly interval-censored data. We propose a fully Bayesian approach coped with efficient Markov chain Monte Carlo methods to fill this gap. A four-stage data augmentation procedure is introduced to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive. The empirical performance of the proposed method is evaluated through two simulation studies, and the model is then applied to a dental health study.

A joint model for multivariate longitudinal and survival data to discover the conversion to Alzheimer's disease.

Alzheimer's disease (AD) is an incurable and progressive disease that starts from mild cognitive impairment and deteriorates over time. Examining the effects of patients' longitudinal cognitive decline on time to conversion to AD and obtaining a reliable diagnostic model are therefore critical to the evaluation of AD prognosis and early treatment. Previous studies either assess patients' cognitive impairment through a single cognitive test or assume it changes linearly across time, thereby leading to an incomplete measure of cognitive decline or overlooking the subtle trajectory pattern of patients' cognitive impairment. This study develops a new joint model to address these shortcomings. First, a dynamic factor analysis model is adopted to characterize cognitive impairment through multiple cognitive measures in a comprehensive manner. Second, a spline-based random coefficient model is proposed to reveal possibly nonlinear trajectories of patients' cognitive decline. Finally, a proportional hazard model is considered to examine the effects of time-invariant markers and time-variant cognitive impairment on AD hazards. A Bayesian approach coupled with spline approximation techniques and MCMC methods is developed to conduct statistical inference. The application of the proposed method to the Alzheimer's Disease Neuroimaging Initiative study provides new insights into the prevention of AD and shows a high prediction capacity of the proposed method.

Dynamic semiparametric transformation models for recurrent event data with a terminal event.

Recurrent event data with a terminal event commonly arise in many longitudinal follow-up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of the recurrent events with a terminal event, where some covariate effects may be time-varying. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, relevant significance tests are suggested for examining whether or not covariate effects vary with time, and a model checking procedure is presented for assessing the adequacy of the proposed models. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is provided.

Joint Hidden Markov Model for Longitudinal and Time-to-Event Data with Latent Variables.

This study develops a new joint modeling approach to simultaneously analyze longitudinal and time-to-event data with latent variables. The proposed model consists of three components. The first component is a hidden Markov model for investigating a longitudinal observation process and its underlying transition process as well as their potential risk factors and dynamic heterogeneity. The second component is a factor analysis model for characterizing latent risk factors through multiple observed variables. The third component is a proportional hazards model for examining the effects of observed and latent risk factors on the hazards of interest. A shared random effect is introduced to allow the longitudinal and time-to-event outcomes to be correlated. A Bayesian approach coupled with efficient Markov chain Monte Carlo methods is developed to conduct statistical inference. The performance of the proposed method is evaluated through simulation studies. An application of the proposed model to a general health survey study concerning cognitive impairment and mortality for Chinese elders is presented.

Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes.

We consider accelerated failure time models with error-prone time-to-event outcomes. The proposed models extend the conventional accelerated failure time model by allowing time-to-event responses to be subject to measurement errors. We describe two measurement error models, a logarithm transformation regression measurement error model and an additive error model with a positive increment, to delineate possible scenarios of measurement error in time-to-event outcomes. We develop Bayesian approaches to conduct statistical inference. Efficient Markov chain Monte Carlo algorithms are developed to facilitate the posterior inference. Extensive simulation studies are conducted to assess the performance of the proposed method, and an application to a study of Alzheimer's disease is presented.

A joint modeling approach for analyzing marker data in the presence of a terminal event.

In many medical studies, markers are contingent on recurrent events and the cumulative markers are usually of interest. However, the recurrent event process is often interrupted by a dependent terminal event, such as death. In this article, we propose a joint modeling approach for analyzing marker data with informative recurrent and terminal events. This approach introduces a shared frailty to specify the explicit dependence structure among the markers, the recurrent, and terminal events. Estimation procedures are developed for the model parameters and the degree of dependence, and a prediction of the covariate-specific cumulative markers is provided. The finite sample performance of the proposed estimators is examined through simulation studies. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is illustrated.

Bayesian latent factor on image regression with nonignorable missing data.

Medical imaging data have been widely used in modern health care, particularly in the prognosis, screening, diagnosis, and treatment of various diseases. In this study, we consider a latent factor-on-image (LoI) regression model that regresses a latent factor on ultrahigh dimensional imaging covariates. The latent factor is characterized by multiple manifest variables through a factor analysis model, while the manifest variables are subject to nonignorable missingness. We propose a two-stage approach for statistical inference. At the first stage, an efficient functional principal component analysis method is applied to reduce the dimension and extract useful features/eigenimages. At the second stage, a factor analysis mode is proposed to characterize the latent response variable. Moreover, an LoI model is used to detect influential risk factors, and an exponential tiling model applied to accommodate nonignoreable nonresponses. A fully Bayesian method with an adjust spike-and-slab absolute shrinkage and selection operator (lasso) procedure is developed for the estimation and selection of influential features/eigenimages. Simulation studies show the proposed method exhibits satisfactory performance. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative data set.

Mixture proportional hazards cure model with latent variables.

A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.

Joint analysis of mixed types of outcomes with latent variables.

We propose a joint modeling approach to investigating the observed and latent risk factors of mixed types of outcomes. The proposed model comprises three parts. The first part is an exploratory factor analysis model that summarizes latent factors through multiple observed variables. The second part is a proportional hazards model that examines the observed and latent risk factors of multivariate time-to-event outcomes. The third part is a linear regression model that investigates the determinants of a continuous outcome. We develop a Bayesian approach coupled with MCMC methods to determine the number of latent factors, the association between latent and observed variables, and the important risk factors of different types of outcomes. A modified stochastic search item selection algorithm, which introduces normal-mixture-inverse gamma priors to factor loadings and regression coefficients, is developed for simultaneous model selection and parameter estimation. The proposed method is subjected to simulation studies for empirical performance assessment and then applied to a study concerning the risk factors of type 2 diabetes and the associated complications.

Regression analysis of current status data with latent variables.

Current status data occur in many fields including demographical, epidemiological, financial, medical, and sociological studies. We consider the regression analysis of current status data with latent variables. The proposed model consists of a factor analytic model for characterizing latent variables through their multiple surrogates and an additive hazard model for examining potential covariate effects on the hazards of interest in the presence of current status data. We develop a borrow-strength estimation procedure that incorporates the expectation-maximization algorithm and correlated estimating equations. The consistency and asymptotic normality of the proposed estimators are established. A simulation study is conducted to evaluate the finite sample performance of the proposed method. A real-life study on the chronic kidney disease of type 2 diabetic patients is presented.

Two-part hidden Markov models for semicontinuous longitudinal data with nonignorable missing covariates.

This study develops a two-part hidden Markov model (HMM) for analyzing semicontinuous longitudinal data in the presence of missing covariates. The proposed model manages a semicontinuous variable by splitting it into two random variables: a binary indicator for determining the occurrence of excess zeros at all occasions and a continuous random variable for examining its actual level. For the continuous longitudinal response, an HMM is proposed to describe the relationship between the observation and unobservable finite-state transition processes. The HMM consists of two major components. The first component is a transition model for investigating how potential covariates influence the probabilities of transitioning from one hidden state to another. The second component is a conditional regression model for examining the state-specific effects of covariates on the response. A shared random effect is introduced to each part of the model to accommodate possible unobservable heterogeneity among observation processes and the nonignorability of missing covariates. A Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure is developed to conduct simultaneous variable selection and estimation. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative dataset. New insights into the pathology of Alzheimer's disease and its potential risk factors are obtained.