Plant regeneration in the new era:from molecular mechanisms to biotechnology applications

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.

An overview of image restoration based on variational regularization

Image restoration is a complicated process in which the original information can be recovered from the degraded image model caused by lots of factors.Mathematically,image restoration problems are ill-posed inverse prob-lems.In this paper image restoration models and algorithms based on variational regularization are surveyed.First,we review and analyze the typical models for denoising,deblurring and inpainting.Second,we construct a unified restoration model based on variational regularization and summarize the typical numerical methods for the model.At last,we point out eight diffcult problems which remain open in this field.

Analysis of Deep Ritz Methods for Semilinear Elliptic Equations

In this paper,we propose a method for solving semilinear elliptical equa-tions using a ResNet with ReLU2 activations.Firstly,we present a comprehensive formulation based on the penalized variational form of the elliptical equations.We then apply the Deep Ritz Method,which works for a wide range of equations.We obtain an upper bound on the errors between the acquired solutions and the true solutions in terms of the depth D,width W of the ReLU2 ResNet,and the num-ber of training samples n.Our simulation results demonstrate that our method can effectively overcome the curse of dimensionality and validate the theoretical results.

Condensation of STM is critical for shoot meristem maintenance and salt tolerance in Arabidopsis

The shoot meristem generates the entire shoot system and is precisely maintained throughout the life cycle under various environmental challenges.In this study,we identified a prion-like domain(PrD)in the key shoot meristem regulator SHOOT MERISTEMLESS(STM),which distinguishes STM from other related KNOX1 proteins.We demonstrated that PrD stimulates STM to form nuclear condensates,which are required for maintaining the shoot meristem.STM nuclear condensate formation is stabilized by selected PrD-containing STM-interacting BELL proteins in vitro and in vivo.Moreover,condensation of STM promotes its interaction with the Mediator complex subunit MED8 and thereby enhances its transcriptional activity.Thus,condensate formation emerges as a novel regulatory mechanism of shoot meristem functions.Furthermore,we found that the formation of STM condensates is enhanced upon salt stress,which allows enhanced salt tolerance and increased shoot branching.Our findings highlight that the transcription factor partitioning plays an important role in cell fate determination and might also act as a tunable environmental acclimation mechanism.

Boosting wheat functional genomics via an indexed EMS mutant library of KN9204

A better understanding of wheat functional genomics can improve targeted breeding for better agronomic traits and environmental adaptation.However,the lack of gene-indexed mutants and the low transformation efficiency of wheat limit in-depth gene functional studies and genetic manipulation for breeding.In this study,we created a library for KN9204,a popular wheat variety in northern China,with a reference genome,transcriptome,and epigenome of different tissues,using ethyl methyl sulfonate(EMS)mutagenesis.This library contains a vast developmental diversity of critical tissues and transition stages.Exome capture sequencing of 2090 mutant lines using KN9204 genome-designed probes revealed that 98.79%of coding genes had mutations,and each line had an average of 1383 EMS-type SNPs.We identified new allelic variations for crucial agronomic trait-related genes such as Rht-D1,Q,TaTB1,and WFZP.We tested 100 lines with severemutations in 80 NAC transcription factors(TFs)under drought and salinity stress and identified 13 lines with altered sensitivity.Further analysis of three lines using transcriptome and chromatin accessibility data revealed hundreds of direct NAC targets with altered transcription patterns under salt or drought stress,including SNAC1,DREB2B,CML16,and ZFP182,factors known to respond to abiotic stress.Thus,we have generated and indexed a KN9204 EMS mutant library that can facilitate functional genomics research and offer resources for genetic manipulation of wheat.

A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs

In this paper,we give the first rigorous error estimation of the Weak Adversarial Neural Networks(WAN)in solving the second order parabolic PDEs.By decomposing the error into approximation error and statistical error,we first show the weak solution can be approximated by the ReLU2 with arbitrary accuracy,then prove that the statistical error can also be efficiently bounded by the Rademacher complexity of the network functions,which can be further bounded by some integral related with the covering numbers and pseudo-dimension of ReLU2 space.Finally,by combining the two bounds,we prove that the error of the WAN method can be well controlled if the depth and width of the neural network as well as the sample numbers have been properly selected.Our result also reveals some kind of freedom in choosing sample numbers on∂Ωand in the time axis.

Towards Plant Synthetic Genomics

Rapid advances in DNA synthesis techniques have allowed the assembly and engineering of viral and microbial genomes.Multicellular eukaryotic organisms,with their larger genomes,abundant transposons,and prevalent epigenetic regulation,present a new frontier to synthetic genomics.Plant synthetic genomics have long been proposed,and exciting progress has been made using the top-down approach.In this perspective,we propose applying bottom-up genome synthesis in multicellular plants,starting from the model moss Physcomitrium patens,in which homologous recombination,DNA delivery,and regeneration are possible,although further optimizations are necessary.We then discuss technical barriers,including genome assembly and plant transformation,associated with synthetic genomics in seed plants.

Improved Analysis of PINNs:Alleviate the CoD for Compositional Solutions

In this paper,we present an improved analysis of the Physics Informed Neural Networks(PINNs)method for solving second-order elliptic equations.By assuming an intrinsic sparse structure in the underlying solution,we provide a convergence rate analysis that can overcome the curse of dimensionality(CoD).Specifically,using some approximation theory in Sobolev space together with the multivariate Faa di Bruno formula,we first derive the approximation error for composition functions with a small degree of freedom in each compositional layer.Furthermore,by integrating several results on the statistical error of neural networks,we obtain a refined convergence rate analysis for PINNs in solving elliptic equations with compositional solutions.We also demonstrate the benefits of the intrinsic sparse structure with two simple numerical examples.

Cox比例风险模型中基于SELO惩罚函数的变量选择方法

在生物医学研究中,研究个体的失效时间往往存在删失,Cox比例风险模型是经常被用来处理此类删失数据的模型.对于带有删失的高维数据,如何从众多协变量中挑选出少数的致病因素是研究者的兴趣所在.本文针对高维删失数据利用SELO惩罚函数考虑了基于Cox比例风险模型框架下的变量选择及参数估计问题.在允许协变量维数发散的条件下,本文给出SELO惩罚估计量的相合性以及oracle性质.计算方面若采用传统方法计算惩罚估计解,当协变量维数较高时计算Hesse阵的逆矩阵需要花费大量的时间,且SELO惩罚函数在原点的不光滑性也给计算SELO惩罚估计带来很大难度.为此,本文利用光滑化技术对SELO惩罚函数进行近似,并利用DFP公式去代替Hesse阵的逆矩阵,进而提出了MSQN算法.模拟计算的结果表明,SELO惩罚方法比已有常用的惩罚方法表现更好,而且本文提出的新算法与常用的坐标下降算法相比表现更优.在真实数据部分,本文还分析了乳腺癌数据,并利用留一交叉验证法来评估预测的好坏.

?~0正则化下衰减信号稀疏恢复的PDASC算法

稀疏恢复的主要任务之一是发展和分析在计算上易于处理的算法以得到欠定线性系统的稀疏解.焦雨领等(2015)提出了一种PDASC算法求解i^0正则化最小二乘问题,并在一定的RIP条件下分析了 PDASC的误差估计,进而建立了有限步全局收敛性.对一类具有强衰减特性的信号,本文将RIP常数条件减弱到与稀疏度T无关.此外,数据驱动的选择准则被提出用来选择适合的正则参数.数值实验验证了 PDASC算法和数据驱动参数选择准则的效率和精度.

带辅助协变量的相关失效时间数据的加权估计伪部分似然方法

本文在不同基准风险边际模型下考虑带辅助协变量的相关失效时间数据的统计推断.假设感兴趣的主协变量仅在全研究队列的一个子集中是精确测量的,而主协变量的辅助协变量则对研究队列的全部个体均可获得.首先利用辅助信息经验地估计相对风险函数,然后提出一种加权估计伪部分似然(weighted estimated pseudo-partial likelihood,WEPPL)方法求边际风险率参数的估计.本文在辅助协变量为分类变量的情形下建立WEPPL估计的渐近性质.相应估计被证明是相合的和渐近正态的.本文通过模拟研究评估提出的估计在有限样本下的表现.结果显示提出的加权估计在效率上要优于未加权的估计,特别是当失效时间之间相关性较强的时候.

Molecular Mechanisms of Leaf Morphogenesis

Plants maintain the ability to form lateral appendages throughout their life cycle and form leaves as the principal lateral appendages of the stem. Leaves initiate at the peripheral zone of the shoot apical meristem and then develop into flattened structures. In most plants, the leaf functions as a solar panel, where photosynthesis converts carbon dioxide and water into carbohydrates and oxygen. To produce structures that can optimally fulfill this function, plants precisely control the initiation, shape, and polarity of leaves. Moreover, leaf development is highly flexible but follows common themes with conserved regulatory mechanisms. Leaves may have evolved from lateral branches that are converted into determinate, flattened structures. Many other plant parts, such as floral organs, are considered specialized leaves, and thus leaf development underlies their morphogenesis. Here, we review recent advances in the understanding of how threedimensional leaf forms are established. We focus on how genes, phytohormones, and mechanical properties modulate leaf development, and discuss these factors in the context of leaf initiation, polarity establishment and maintenance, leaf flattening, and intercalary growth.

Plant multiscale networks:charting plant connectivity by multi-level analysis and imaging techniques

In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation.Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes.Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project.In plant science,network analysis has similarly been applied to study the connectivity of plant components at the molecular,subcellular,cellular,organic,and organism levels.Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype.In this review,we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities.We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants.Finally,we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.

A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs

In recent years,physical informed neural networks(PINNs)have been shown to be a powerful tool for solving PDEs empirically.However,numerical analysis of PINNs is still missing.In this paper,we prove the convergence rate to PINNs for the second order elliptic equations with Dirichlet boundary condition,by establishing the upper bounds on the number of training samples,depth and width of the deep neural networks to achieve desired accuracy.The error of PINNs is decomposed into approximation error and statistical error,where the approximation error is given in C2 norm with ReLU^(3)networks(deep network with activation function max{0,x^(3)})and the statistical error is estimated by Rademacher complexity.We derive the bound on the Rademacher complexity of the non-Lipschitz composition of gradient norm with ReLU^(3)network,which is of immense independent interest.

A near-complete assembly of an Arabidopsis thaliana genome

The genome sequence of Arabidopsis thaliana,a widely adopted model species,has greatly expedited plant molecular biology research.Over 20 years after the first release of the genome sequence(Arabidopsis Genome Initiative,2000),there remains unresolved gap regions that are presumably composed of highly repetitive sequences,such as telomeres,centromeres,5S rDNA clusters,and nucleolar organizerregions(NORs)containing 45S rDNA.

Convergence Rate Analysis for Deep Ritz Method

Using deep neural networks to solve PDEs has attracted a lot of attentions recently.However,why the deep learning method works is falling far behind its empirical success.In this paper,we provide a rigorous numerical analysis on deep Ritz method(DRM)[47]for second order elliptic equations with Neumann boundary conditions.We establish the first nonasymptotic convergence rate in H^(1)norm for DRM using deep networks with ReLU^(2)activation functions.In addition to providing a theoretical justification of DRM,our study also shed light on how to set the hyperparameter of depth and width to achieve the desired convergence rate in terms of number of training samples.Technically,we derive bound on the approximation error of deep ReLU^(2)network in C^(1)norm and bound on the Rademacher complexity of the non-Lipschitz composition of gradient norm and ReLU^(2)network,both of which are of independent interest.

A crosstalk between auxin and brassinosteroid regulates leaf shape by modulating growth anisotropy

Leaf shape is highly variable within and among plant species,ranging from slender to oval shaped.This is largely determined by the proximodistal axis of growth.However,little is known about how proximal–distal growth is controlled to determine leaf shape.Here,we show that Arabidopsis leaf and sepal proximodistal growth is tuned by two phytohormones.Two class A AUXIN RESPONSE FACTORs(ARFs),ARF6 and ARF8,activate the transcription of DWARF4,which encodes a key brassinosteroid(BR)biosynthetic enzyme.At the cellular level,the phytohormones promote more directional cell expansion along the proximodistal axis,as well as final cell sizes.BRs promote the demethyl-esterification of cell wall pectins,leading to isotropic in-plane cell wall loosening.Notably,numerical simulation showed that isotropic cell wall loosening could lead to directional cell and organ growth along the proximodistal axis.Taken together,we show that auxin acts through biosynthesis of BRs to determine cell wall mechanics and directional cell growth to generate leaves of variable roundness.

The 35S promoter-driven mDII auxin control sensor is uniformly distributed in leaf primordia

The DII auxin sensor has been an invaluable tool for mapping the spatiotemporal auxin response and distribution in the model plant Arabidopsis thaliana.The DII sensor and the m DII control sensor are driven by the widely used constitutive 35S promoter.

Epidermal restriction confers robustness to organ shapes

The shape of comparable tissues and organs is consistent among individuals of a given species,but how this consistency or robustness is achieved remains an open question.The interaction between morphogenetic factors determines organ formation and subsequent shaping,which is ultimately a mechanical process.Using a computational approach,we show that the epidermal layer is essential for the robustness of organ geometry control.Specifically,proper epidermal restriction allows organ asymmetry maintenance,and the tensile epidermal layer is sufficient to suppress local variability in growth,leading to shape robustness.The model explains the enhanced organ shape variations in epidermal mutant plants.In addition,differences in the patterns of epidermal restriction may underlie the initial establishment of organ asymmetry.Our results show that epidermal restriction can answer the longstanding question of how cellular growth noise is averaged to produce precise organ shapes,and the findings also shed light on organ asymmetry establishment.

May the Force Be with You: Overlooked Mechanical Signaling

Within multicellular plants, cells constantly communicate with their neighbors and even distant cells. A cell may produce chemical signals, such as peptides and small molecules, and perceive chemical signals from other cells. Thus, signaling chemicals mediate the exchange of information between cells. There is a simpler and faster way for cells to communicate, i.e., a cell can mechanically nudge its neighbors to send a signal. However, mechanical force is often ignored by biologists, partially due to the difficulty in directly visualizing the force, in contrast to visual detection of signaling molecules.