Redundant and specific roles of individual MIR172 genes in plant development.

Evolutionarily conserved microRNAs (miRNAs) usually have high copy numbers in the genome. The redundant and specific roles of each member of a multimember miRNA gene family are poorly understood. Previous studies have shown that the miR156-SPL-miR172 axis constitutes a signaling cascade in regulating plant developmental transitions. Here, we report the feasibility and utility of CRISPR-Cas9 technology to investigate the functions of all 5 MIR172 family members in Arabidopsis. We show that an Arabidopsis plant devoid of miR172 is viable, although it displays pleiotropic morphological defects. MIR172 family members exhibit distinct expression pattern and exert functional specificity in regulating meristem size, trichome initiation, stem elongation, shoot branching, and floral competence. In particular, we find that the miR156-SPL-miR172 cascade is bifurcated into specific flowering responses by matching pairs of coexpressed SPL and MIR172 genes in different tissues. Our results thus highlight the spatiotemporal changes in gene expression that underlie evolutionary novelties of a miRNA gene family in nature. The expansion of MIR172 genes in the Arabidopsis genome provides molecular substrates for the integration of diverse floral inductive cues, which ensures that plants flower at the optimal time to maximize seed yields.

A spatiotemporally regulated transcriptional complex underlies heteroblastic development of leaf hairs in Arabidopsis thaliana.

Heteroblasty refers to a phenomenon that a plant produces morphologically or functionally different lateral organs in an age-dependent manner. In the model plant Arabidopsis thaliana, the production of trichomes (epidermal leaf hairs) on the abaxial (lower) side of leaves is a heteroblastic mark for the juvenile-to-adult transition. Here, we show that the heteroblastic development of abaxial trichomes is regulated by a spatiotemporally regulated complex comprising the leaf abaxial fate determinant (KAN1) and the developmental timer (miR172-targeted AP2-like proteins). We provide evidence that a short-distance chromatin loop brings the downstream enhancer element into close association with the promoter elements of GL1, which encodes a MYB transcription factor essential for trichome initiation. During juvenile phase, the KAN1-AP2 repressive complex binds to the downstream sequence of GL1 and represses its expression through chromatin looping. As plants age, the gradual reduction in AP2-like protein levels leads to decreased amount of the KAN1-AP2 complex, thereby licensing GL1 expression and the abaxial trichome initiation. Our results thus reveal a novel molecular mechanism by which a heteroblastic trait is governed by integrating age and leaf polarity cue in plants.

Semiparametric function-on-function quantile regression model with dynamic single-index interactions.

In this paper we propose a new semiparametric function-on-function quantile regression model with time-dynamic single-index interactions. Our model is very flexible in taking into account of the nonlinear time-dynamic interaction effects of the multivariate longitudinal/functional covariates on the longitudinal response, that most existing quantile regression models for longitudinal data are special cases of our proposed model. We propose to approximate the bivariate nonparametric coefficient functions by tensor product B-splines, and employ a check loss minimization approach to estimate the bivariate coefficient functions and the index parameter vector. Under some mild conditions, we establish the asymptotic normality of the estimated single-index coefficients using projection orthogonalization technique, and obtain the convergence rates of the estimated bivariate coefficient functions. Furthermore, we propose a score test to examine whether there exist interaction effects between the covariates. The finite sample performance of the proposed method is illustrated by Monte Carlo simulations and an empirical data analysis.

Antifungal activities of Bacillus velezensis FJAT-52631 and its lipopeptides against anthracnose pathogen Colletotrichum acutatum.

This study was aim at investigating antifungal activities of Bacillus velezensis FJAT-52631 and its lipopeptides against Colletotrichum acutatum ex situ and in situ. The results showed that the strain FJAT-52631 and its crude lipopeptides (10 mg/ml) exhibited strong inhibitory effects on growth of C. acutatum FJAT-30256 with an inhibition rate of 75.3% and an inhibition zone diameter of 17.66 mm, respectively. Both the viable bacterial cultures and lipopeptides of FJAT-52631 could delay the onset of loquat anthracnose by 1 day and lower the incidence of loquat anthracnose in situ. The whole cultures of B. velezensis FJAT-52631 displayed a 50% biocontrol efficacy on loquat anthracnose at the fourth day after inoculation, but the crude lipopeptides not. The average lesion diameter of the whole-culture treated group was 5.62 mm, which was smaller than that of control group (6.81 mm). All the three types of lipopeptides including iturin A, fengycin, and surfactin A secreted from the strain FJAT-52631 exhibited antifungal activities. Among them, surfactin A displayed higher antifungal activity at a concentration of 1.25 mg/mL than other two lipopeptides even if at a concentration of 60 mg/mL. Thus, the results indicated that surfactin A produced by FJAT-52631 played a major role in the biocontrol of the loquat anthracnose. Scanning electron microscopy (SEM) observation revealed the structural deformities in the mycelia of C. acutatum. The above results suggested that the antifungal lipopeptides from B. velezensis FJAT-52631 would be potential in biocontrol against anthracnose disease of loquat caused by C. acutatum.

Communication-efficient estimation and inference for high-dimensional quantile regression based on smoothed decorrelated score.

Distributed estimation based on different sources of observations has drawn attention in the modern statistical learning. In practice, due to the expensive cost or time-consuming process to collect data in some cases, the sample size on each local site can be small, but the dimension of covariates is large and may be far larger than the sample size on each site. In this article, we focus on the distributed estimation and inference for a preconceived low-dimensional parameter vector in the high-dimensional quantile regression model with small local sample size. Specifically, we consider that the data are inherently distributed and propose two communication-efficient estimators by generalizing the decorrelated score approach to conquer the slow convergence rate of nuisance parameter estimation and adopting the smoothing technique based on multiround algorithms. The risk bounds and limiting distributions of the proposed estimators are given. The finite sample performance of the proposed estimators is studied through simulations and an application to a gene expression dataset is also presented.

Ultra high-dimensional semiparametric longitudinal data analysis.

As ultra high-dimensional longitudinal data are becoming ever more apparent in fields such as public health and bioinformatics, developing flexible methods with a sparse model is of high interest. In this setting, the dimension of the covariates can potentially grow exponentially as exp(n1/2) with respect to the number of clusters n. We consider a flexible semiparametric approach, namely, partially linear single-index models, for ultra high-dimensional longitudinal data. Most importantly, we allow not only the partially linear covariates but also the single-index covariates within the unknown flexible function estimated nonparametrically to be ultra high dimensional. Using penalized generalized estimating equations, this approach can capture correlation within subjects, can perform simultaneous variable selection and estimation with a smoothly clipped absolute deviation penalty, and can capture nonlinearity and potentially some interactions among predictors. We establish asymptotic theory for the estimators including the oracle property in ultra high dimension for both the partially linear and nonparametric components, and we present an efficient algorithm to handle the computational challenges. We show the effectiveness of our method and algorithm via a simulation study and a yeast cell cycle gene expression data.

Long Noncoding RNA NHEG1 Drives ß-Catenin Transactivation and Neuroblastoma Progression through Interacting with DDX5.

Recent studies suggest that long noncoding RNAs (lncRNAs) play essential roles in tumor progression. However, the functional roles and underlying mechanisms of lncRNAs in neuroblastoma (NB), the most common malignant solid tumor in pediatric population, still remain elusive. Herein, through integrating analysis of a public RNA sequencing dataset, neuroblastoma highly expressed 1 (NHEG1) was identified as a risk-associated lncRNA, contributing to an unfavorable outcome of NB. Depletion of NHEG1 led to facilitated differentiation and decreased growth and aggressiveness of NB cells. Mechanistically, NHEG1 bound to and stabilized DEAD-box helicase 5 (DDX5) protein through repressing proteasome-mediated degradation, resulting in ß-catenin transactivation that altered target gene expression associated with NB progression. We further determined a lymphoid enhancer binding factor 1 (LEF1)/transcription factor 7-like 2 (TCF7L2)/NHEG1/DDX5/ß-catenin axis with a positive feedback loop and demonstrated that NHEG1 harbored oncogenic properties via its interplay with DDX5. Administration of small interfering RNAs against NHEG1 or DDX5 reduced tumor growth and prolonged survival of nude mice bearing xenografts. High NHEG1 or DDX5 expression was associated with poor survival of NB patients. These results indicate that lncRNA NHEG1 exhibits oncogenic activity that affects NB progression via stabilizing the DDX5 protein, which might serve as a potential therapeutic target for NB.

MicroRNA166 Monitors SPOROCYTELESS/NOZZLE for Building of the Anther Internal Boundary.

The internal boundary between inner and outer microsporangia within anthers is essential for male fertility of vascular plants. Dehiscence zones embedded in the boundary release pollen for fertilization. However, the molecular mechanism underlying boundary formation in anthers remains poorly understood. Here, we report that microRNA166 (miR166) and its target PHABULOSA (PHB) regulate SPOROCYTELESS/NOZZLE (SPL/NZZ), which controls microsporogenesis. In developing anthers of Arabidopsis (Arabidopsis thaliana), the expression domains of miR165/6 and SPL/NZZ are overlapped and rearranged synchronously. Dominant mutation of PHB suppresses SPL/NZZ expression on the adaxial sides of stamens, resulting in a thickened boundary, whereas activation of MIR166g up-regulates SPL/NZZ expression, leading to ectopic microsporogenesis in the boundary. PHB limits the expression domains of SPL/NZZ to facilitate construction of the boundary, while miR166 preserves the expression domains of SPL/NZZ by inhibiting PHB to allow the inner microsporangia to take shape. Subsequently, PHB activates the key stem cell maintainer WUSCHEL in anthers to restrict the stomium cells to the boundary so that dehiscence zones develop and release pollen properly. These findings link adaxial/abaxial polarity to microsporogenesis in building of the internal boundary of anthers and thus advance the concepts underlying the establishment of the internal structure of male organs.

A Two-Step Model for de Novo Activation of WUSCHEL during Plant Shoot Regeneration.

Plant cells are totipotent and competent to regenerate from differentiated organs. It has been known for six decades that cytokinin-rich medium induces shoot regeneration from callus cells. However, the underlying molecular mechanism remains elusive. The homeodomain transcription factor WUSCHEL (WUS) is essential for de novo establishment of the shoot stem cell niche in Arabidopsis thaliana We found that WUS-positive (WUS+) cells mark the shoot progenitor region during regeneration. A cytokinin-rich environment initially promotes the removal of the repressive histone mark H3K27me3 at the WUS locus in a cell cycle-dependent manner. Subsequently, the B-type ARABIDOPSIS RESPONSE REGULATORs (ARRs) ARR1, ARR2, ARR10, and ARR12, which function as transcriptional activators in the cytokinin signaling pathway, spatially activate WUS expression through binding with microRNA165/6-targeted HD-ZIP III transcription factors. Thus, our results provide important insights into the molecular framework for cytokinin-directed shoot regeneration and reveal a two-step mechanism for de novo activation of WUS.

An intrinsic microRNA timer regulates progressive decline in shoot regenerative capacity in plants.

Plant cells are totipotent and competent to regenerate from differentiated organs. It has been shown that two phytohormones, auxin and cytokinin, play critical roles within this process. As in animals, the regenerative capacity declines with age in plants, but the molecular basis for this phenomenon remains elusive. Here, we demonstrate that an age-regulated microRNA, miR156, regulates shoot regenerative capacity. As a plant ages, the gradual increase in miR156-targeted SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) transcription factors leads to the progressive decline in shoot regenerative capacity. In old plants, SPL reduces shoot regenerative capacity by attenuating the cytokinin response through binding with the B-type ARABIDOPSIS RESPONSE REGULATORs, which encode the transcriptional activators in the cytokinin signaling pathway. Consistently, the increased amount of exogenous cytokinin complements the reduced shoot regenerative capacity in old plants. Therefore, the recruitment of age cues in response to cytokinin contributes to shoot regenerative competence.

A partially linear additive model for clustered proportion data.

Proportion data with support lying in the interval [0,1] are a commonplace in various domains of medicine and public health. When these data are available as clusters, it is important to correctly incorporate the within-cluster correlation to improve the estimation efficiency while conducting regression-based risk evaluation. Furthermore, covariates may exhibit a nonlinear relationship with the (proportion) responses while quantifying disease status. As an alternative to various existing classical methods for modeling proportion data (such as augmented Beta regression) that uses maximum likelihood, or generalized estimating equations, we develop a partially linear additive model based on the quadratic inference function. Relying on quasi-likelihood estimation techniques and polynomial spline approximation for unknown nonparametric functions, we obtain the estimators for both parametric part and nonparametric part of our model and study their large-sample theoretical properties. We illustrate the advantages and usefulness of our proposition over other alternatives via extensive simulation studies, and application to a real dataset from a clinical periodontal study.

Kernel-Based Decentralized Policy Evaluation for Reinforcement Learning.

We investigate the decentralized nonparametric policy evaluation problem within reinforcement learning (RL), focusing on scenarios where multiple agents collaborate to learn the state-value function using sampled state transitions and privately observed rewards. Our approach centers on a regression-based multistage iteration technique employing infinite-dimensional gradient descent (GD) within a reproducing kernel Hilbert space (RKHS). To make computation and communication more feasible, we employ Nyström approximation to project this space into a finite-dimensional one. We establish statistical error bounds to describe the convergence of value function estimation, marking the first instance of such analysis within a fully decentralized nonparametric framework. We compare the regression-based method to the kernel temporal difference (TD) method in some numerical studies.

Single-Index Mixed-Effects Model for Asymmetric Bivariate Clustered Data.

Studies/trials assessing status and progression of periodontal disease (PD) usually focus on quantifying the relationship between the clustered (tooth within subjects) bivariate endpoints, such as probed pocket depth (PPD), and clinical attachment level (CAL) with the covariates. Although assumptions of multivariate normality can be invoked for the random terms (random effects and errors) under a linear mixed model (LMM) framework, violations of those assumptions may lead to imprecise inference. Furthermore, the response-covariate relationship may not be linear, as assumed under a LMM fit, and the regression estimates obtained therein do not provide an overall summary of the risk of PD, as obtained from the covariates. Motivated by a PD study on Gullah-speaking African-American Type-2 diabetics, we cast the asymmetric clustered bivariate (PPD and CAL) responses into a non-linear mixed model framework, where both random terms follow the multivariate asymmetric Laplace distribution (ALD). In order to provide a one-number risk summary, the possible non-linearity in the relationship is modeled via a single-index model, powered by polynomial spline approximations for index functions, and the normal mixture expression for ALD. To proceed with a maximum-likelihood inferential setup, we devise an elegant EM-type algorithm. Moreover, the large sample theoretical properties are established under some mild conditions. Simulation studies using synthetic data generated under a variety of scenarios were used to study the finite-sample properties of our estimators, and demonstrate that our proposed model and estimation algorithm can efficiently handle asymmetric, heavy-tailed data, with outliers. Finally, we illustrate our proposed methodology via application to the motivating PD study.

HYL1 is required for establishment of stamen architecture with four microsporangia in Arabidopsis.

The stamen produces pollen grains for pollination in higher plants. Coordinated development of four microsporangia in the stamen is essential for normal fertility. The roles of miR165/166-directed pathways in the establishment of adaxial-abaxial polarity have been well defined in leaves. However, the molecular mechanism underlying the adaxial-abaxial polarity of the stamen is elusive. Here it is reported that HYPONASTIC LEAVES1 (HYL1), a general regulator of microRNA (miRNA) biogenesis, plays an essential role in establishing the stamen architecture of the four microsporangia in Arabidopsis thaliana. In stamens, HYL1 and miR165/6 expression are progressively restricted to the lateral region, microsporangia, microspore mother cells, and microspores, whereas HD-ZIP III genes are preferentially expressed in the middle region, vascular bundle, and stomium. Loss of HYL1 leads to the formation of two rather than four microsporangia in each stamen. In the stamen of the hyl1 mutant, miR165/6 accumulation is reduced, whereas miR165/6-targeted HD-ZIP III genes are up-regulated and FILAMENTOUS FLOWER (FIL) is down-regulated; and, specifically, REVOLUTA (REV) is overexpressed in the adaxial region and FIL is underexpressed in the abaxial regions, concomitant with the aberrance of the two inner microsporangia and partial adaxialization of the connectives. Genetic analysis reveals that FIL works downstream of HYL1, and the defects in hyl1 stamens are partially rescued by rev-9 or phv-5 phb-6 alleles. These results suggest that HYL1 modulates inner microsporangia and stamen architecture by repression of HD-ZIP III genes and promotion of the FIL gene through miR165/6. Thus, the role of HYL1 in establishment of stamen architecture provides insight into the molecular mechanism of male fertility.

Partially linear structure selection in Cox models with varying coefficients.

To explore the nonlinear interactions between covariates and an index variable, partially linear proportional hazards models have been proposed for censored survival data. However, specification of the partially linear structure was usually carried out in an ad-hoc manner by first fitting a full varying-coefficient model and visually examining the resulting fit to identify the linear part. In this article, we consider the problem of coefficient estimation and constant coefficient identification based on a double shrinkage approach. Variable selection is also considered in a coherent estimation framework, resulting in a double-penalization procedure. Under the mild assumptions, we establish asymptotic properties for the procedure such as consistency, sparesistency, constansistency, and asymptotic normality. We evaluate the performance of the proposed method by numerical simulations and demonstrate its application using a breast cancer data set.

On Optimal Learning With Random Features.

We consider supervised learning in a reproducing kernel Hilbert space (RKHS) using random features. We show that the optimal rate is obtained under suitable regularity conditions, and at the same time improving on the existing bounds on the number of random features required. As a straightforward extension, distributed learning in the simple setting of one-shot communication is also considered that achieves the same optimal rate.

Value iteration for streaming data on a continuous space with gradient method in an RKHS.

The classical theory of reinforcement learning focused on the tabular setting when states and actions are finite, or for linear representation of the value function in a finite-dimensional approximation. Establishing theory on general continuous state and action space requires a careful treatment of complexity theory of appropriately chosen function spaces and the iterative update of the value function when stochastic gradient descent (SGD) is used. For the classical prediction problem in reinforcement learning based on i.i.d. streaming data in the framework of reproducing kernel Hilbert spaces, we establish polynomial sample complexity taking into account the smoothness of the value function. In particular, we prove that the gradient descent algorithm efficiently computes the value function with appropriately chosen step sizes, with a convergence rate that can be close to 1/N, which is the best possible rate for parametric SGD. The advantages of using the gradient descent algorithm include its computational convenience and it can naturally deal with streaming data.

Properties of Standard and Sketched Kernel Fisher Discriminant.

Kernel Fisher discriminant (KFD) is a popular tool as a nonlinear extension of Fisher's linear discriminant, based on the use of the kernel trick. However, its asymptotic properties are still rarely studied. We first present an operator-theoretical formulation of KFD which elucidates the population target of the estimation problem. Convergence of the KFD solution to its population target is then established. However, the complexity of finding the solution poses significant challenges when n is large and we further propose a sketched estimation approach based on a m×n sketching matrix which possesses the same asymptotic properties (in terms of convergence rate) even when m is much smaller than n. Some numerical results are presented to illustrate the performances of the sketched estimator.

Sex differences in the oral microbiome, host traits, and their causal relationships.

The oral microbiome has been implicated in a growing number of diseases; however, determinants of the oral microbiome and their roles remain elusive. Here, we investigated the oral (saliva and tongue dorsum) metagenome, the whole genome, and other omics data in a total of 4,478 individuals and demonstrated that the oral microbiome composition and its major contributing host factors significantly differed between sexes. We thus conducted a sex-stratified metagenome-genome-wide-association study (M-GWAS) and identified 11 differential genetic associations with the oral microbiome (p sex-difference  < 5 × 10-8). Furthermore, we performed sex-stratified Mendelian randomization (MR) analyses and identified abundant causalities between the oral microbiome and serum metabolites. Notably, sex-specific microbes-hormonal interactions explained the mostly observed sex hormones differences such as the significant causalities enrichments for aldosterone in females and androstenedione in males. These findings illustrate the necessity of sex stratification and deepen our understanding of the interplay between the oral microbiome and serum metabolites.

Identification of partially linear structure in additive models with an application to gene expression prediction from sequences.

The additive model is a semiparametric class of models that has become extremely popular because it is more flexible than the linear model and can be fitted to high-dimensional data when fully nonparametric models become infeasible. We consider the problem of simultaneous variable selection and parametric component identification using spline approximation aided by two smoothly clipped absolute deviation (SCAD) penalties. The advantage of our approach is that one can automatically choose between additive models, partially linear additive models and linear models, in a single estimation step. Simulation studies are used to illustrate our method, and we also present its applications to motif regression.