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Plants or tissues can be regenerated through various pathways. Like animal regeneration, cell totipotency and pluripotency are the molecular basis of plant regeneration. Detailed systematic studies on Arabidopsis thaliana gradually unravel the fundamental mechanisms and principles underlying plant regeneration. Specifically, plant hormones, cell division, epigenetic remodeling, and transcription factors play crucial roles in reprogramming somatic cells and reestablishing meristematic cells. Recent research on basal non-vascular plants and monocot crops has revealed that plant regeneration differs among species, with various plant species using distinct mechanisms and displaying significant differences in regenerative capacity. Conducting multi-omics studies at the single-cell level, tracking plant regeneration processes in real-time, and deciphering the natural variation in regenerative capacity will ultimately help understand the essence of plant regeneration, improve crop regeneration efficiency, and contribute to future crop design. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study multiple structural changes that occur at unknown locations in high-dimensional linear regression. We consider a structural change model where the parameters are subject to shifts at possibly different locations. We propose a penalized least squares approach, combined with a temporal difference penalty term for the difference between the coefficients at successive points for identifying latent change points, as well as a common sparsity penalty to detect important covariates. This procedure automatically estimates the number and the locations of the change-points and the parameters in each corresponding segment. To implement the proposed approach, we devise an alternating direction method of multipliers (ADMM) algorithm. We demonstrate the convergence of the proposed ADMM algorithm in the present setting. We also establish an oracle inequality for the proposed estimator. We carry out simulation studies to evaluate the finite sample performance of the proposed method and illustrate its application on a data set. [ABSTRACT FROM AUTHOR]

In this paper, we investigate solving the elliptical multiple eigenvalue (EME) problems using a Feedforward Neural Network. Firstly, we propose a general formulation for computing EME based on penalized variational forms of elliptical eigenvalue problems. Next, we solve the penalized variational form using the Deep Ritz Method. We establish an upper bound on the error between the estimated eigenvalues and true ones in terms of the depth D , width W of the neural network, and training sample size n. By exploring the regularity of the EME and selecting an appropriate depth D and width W , we demonstrate that the desired bound enjoys a convergence rate of O (1 / n 16) , which circumvents the curse of dimensionality. We also present several high-dimensional simulation results to illustrate the effectiveness of our proposed method and support our theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, we consider recovering n - dimensional signals from m binary measurements corrupted by noises and sign flips under the assumption that the target signals have low generative intrinsic dimension, i.e., the target signals can be approximately generated via an L-Lipschitz generator G : R k → R n , k ≪ n . Although the binary measurements model is highly nonlinear, we propose a least square decoder and prove that, up to a constant c, with high probability, the least square decoder achieves a sharp estimation error C k log (L n) m as long as m ≥ C (k log (L n)) . Extensive numerical simulations and comparisons with state-of-the-art methods demonstrated the least square decoder is robust to noise and sign flips, as indicated by our theory. By constructing a ReLU network with properly chosen depth and width, we verify the (approximately) deep generative prior, which is of independent interest. [ABSTRACT FROM AUTHOR]

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms. Methods that perform data integration are needed to analyse spatial transcriptomics data from multiple tissue slides. Here, the authors present PRECAST, an efficient data integration method for multiple spatial transcriptomics datasets with complex batch or biological effects between slides. [ABSTRACT FROM AUTHOR]

We propose a fast Newton algorithm for ℓ 0 regularized high-dimensional generalized linear models based on support detection and root finding. We refer to the proposed method as GSDAR. GSDAR is developed based on the KKT conditions for ℓ 0 -penalized maximum likelihood estimators and generates a sequence of solutions of the KKT system iteratively. We show that GSDAR can be equivalently formulated as a generalized Newton algorithm. Under a restricted invertibility condition on the likelihood function and a sparsity condition on the regression coefficient, we establish an explicit upper bound on the estimation errors of the solution sequence generated by GSDAR in supremum norm and show that it achieves the optimal order in finite iterations with high probability. Moreover, we show that the oracle estimator can be recovered with high probability if the target signal is above the detectable level. These results directly concern the solution sequence generated from the GSDAR algorithm, instead of a theoretically defined global solution. We conduct simulations and real data analysis to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Axillary meristems (AMs) are located in the leaf axil and can establish new growth axes. Whereas their neighboring cells are differentiated, the undifferentiated cells in the AM endow the AM with the same developmental potential as the shoot apical meristem. The AM is, therefore, an excellent system to study stem cell fate maintenance in plants. In this review, we summarize the current knowledge of AM initiation. Recent findings have shown that AMs derive from a stem cell lineage that is maintained in the leaf axil. This review covers AM progenitor cell fate maintenance, reactivation, and meristem establishment. We also highlight recent work that links transcription factors, phytohormones, and epigenetic regulation to AM initiation. [ABSTRACT FROM AUTHOR]

The seamless-L0 (SELO) penalty is a smooth function that very closely resembles the L0 penalty, which has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection. In this paper, the authors first generalize the SELO penalty to a class of penalties retaining good features of SELO, and then develop variable selection and parameter estimation in Cox models using the proposed generalized SELO (GSELO) penalized log partial likelihood (PPL) approach. The authors show that the GSELO-PPL procedure possesses the oracle property with a diverging number of predictors under certain mild, interpretable regularity conditions. The entire path of GSELO-PPL estimates can be efficiently computed through a smoothing quasi-Newton (SQN) with continuation algorithm. The authors propose a consistent modified BIC (MBIC) tuning parameter selector for GSELO-PPL, and show that under some regularity conditions, the GSELOPPL- MBIC procedure consistently identifies the true model. Simulation studies and real data analysis are conducted to evaluate the finite sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

A natural image u is often sparse under a given transformation W, one can use L0 norm of Wu as a regularisation term in image reconstructions. Since minimizing the L0 norm is a NP hard problem, the L1 norm is widely used as an replacement. However, recent studies show that nonconvex penalties, e.g., MCP, enjoy better performance for sparse signal recovery. In this paper, we propose a nonconvex model for image restoration with a minimax concave penalty (MCP). First we establish the existence of a global minimizer for the nonconvex model. Then we solve this model by using the alternating direction method of multipliers algorithm. The convergence of the proposed algorithm is analysed with properly chosen parameters. Numerical experiments show that the MCP model outperforms TV model in terms of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Correction to: I Nature Communications i https://doi.org/10.1038/s41467-023-35947-w, published online 18 January 2023 In this article, the funding from the 'University Development Fund (UDF01003033) from The Chinese University of Hong Kong, Shenzhen' was omitted. These authors contributed equally: Wei Liu, Xu Liao. [Extracted from the article]

We investigate an inverse problem of identifying a Robin coefficient with a sparse structure in the Laplace equation from noisy boundary measurements. The sparse structure of the Robin coefficient γ is understood as a small perturbation of a reference profile γ in the sense that their difference γ− γ has a small support. This problem is formulated as an optimal control problem with an L-regularization term. An iteratively reweighted least-squares algorithm with an inner semismooth Newton iteration is employed to solve the resulting optimization problem, and the convergence of the iteratively weighted least-squares algorithm is established. Numerical results for two-dimensional problems are presented to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to image deblurring, on the basis of total variation (TV) and wavelet frame. The Rudin-Osher-Fatemi model, which is based on TV minimization, has been proven effective for image restoration. The explicit exploitation of sparse approximations of natural images has led to the success of wavelet frame approach in solving image restoration problems. However, TV introduces staircase effects. Thus, we propose a new objective functional that combines the tight wavelet frame and TV to reconstruct images from blurry and noisy observations while mitigating staircase effects. The minimization of the new objective functional presents a computational challenge. We propose a fast minimization algorithm by employing the augmented Lagrangian technique. The experiments on a set of image deblurring benchmark problems show that the proposed method outperforms the previous state-of-the-art methods for image restoration. [ABSTRACT FROM AUTHOR]

Cell is the functional unit of life. To study the complex interactions of systems of biological molecules, it is crucial to dissect these molecules at the cell level. In recent years, major progresses have been made by plant biologists to profile gene expression in specific cell types at the genome-wide level. Approaches based on the isolation of cells, polysomes or nuclei have been developed and successfully used for studying the cell types from distinct organs of several plant species. These cell-level data sets revealed previously unrecognized cellular properties, such as cell-specific gene expression modules and hormone response centers, and should serve as essential resources for functional genomic analyses. Newly developed technologies are more affordable to many laboratories and should help to provide new insights at the cellular resolution in the near future. [ABSTRACT FROM AUTHOR]

Plants have evolved complex and sophisticated transcriptional networks that mediate developmental changes in response to light. These light-regulated processes include seedling photomorphogenesis, seed germination and the shade-avoidance and photoperiod responses. Understanding the components and hierarchical structure of the transcriptional networks that are activated during these processes has long been of great interest to plant scientists. Traditional genetic and molecular approaches have proved powerful in identifying key regulatory factors and their positions within these networks. Recent genomic studies have further revealed that light induces massive reprogramming of the plant transcriptome, and that the early light-responsive genes are enriched in transcription factors. These combined approaches provide new insights into light-regulated transcriptional networks. [ABSTRACT FROM AUTHOR]

Gene regulatory networks control development via domain-specific gene expression. In seed plants, self-renewing stem cells located in the shoot apical meristem (SAM) produce leaves from the SAM peripheral zone. After initiation, leaves develop polarity patterns to form a planar shape. Here we compare translating RNAs among SAM and leaf domains. Using translating ribosome affinity purification and RNA sequencing to quantify gene expression in target domains, we generate a domain-specific translatome map covering representative vegetative stage SAM and leaf domains. We discuss the predicted cellular functions of these domains and provide evidence that dome seemingly unrelated domains, utilize common regulatory modules. Experimental follow up shows that the RABBIT EARS and HANABA TARANU transcription factors have roles in axillary meristem initiation. This dataset provides a community resource for further study of shoot development and response to internal and environmental signals. The shoot apical meristem (SAM) maintains stem cells and generates new leaves and flowers from its periphery. Here via spatially resolved translatome profiling, Tian et al. define distinct molecular signatures of different SAM and leaf domains and identify regulators of axillary meristem initiation. [ABSTRACT FROM AUTHOR]