pleiotropic effects of genes involved in cell wall lignification on agronomic characters

Brown midrib (bm) mutants in maize, which are characterized with altered lignin composition, reduced lignin content and thus with enhanced cell wall digestibility (CWD), are often associated with inferior agronomic traits. To understand how the undesirable associations happen would help us better design strategies to manipulate the cell wall lignification for CWD enhancement without sacrificing biomass yield. First, I reviewed the current knowledge and approaches to study the nature of trait correlations. We proposed that dissection of the trait correlations into DNA polymorphism level is beneficial for plant breeding as intergenic, intragenic, or true pleiotropy will have different impact. Secondly, three new bm mutants were identified by allelism test and were designated as bm5, bm6, and bm7. Then I focused on characterization of bm6, which was revealed to increase CWD but suppress plant height in F2 population. With large mapping populations with about 1000 brown F2 plants, its underlying gene was delimited into a ~180kb interval referring to B73 genome, wherein 10 predicted gene models reside. Besides using natural mutants, we also employed candidate association approach, which suggested that the pleiotropic effects of monolignol biosynthetic genes are most likely due to intragenic linkage of quantitative trait polymorphisms (QTPs). Therefore, optimal haplotypes which combine QTPs beneficial for both CWD and biomass yield might exist. In order to build up suitable materials to confirm this finding, we characterized the COMT gene sampled from the germplasm enhancement of maize (GEM). Much higher genetic diversity of COMT alleles at both DNA and predicted amino acid level and extensive lower linkage disequilibrium (LD) were observed in GEM than in inbred lines. The higher genetic variation suggests GEM is a valuable genetic resource to broaden genetic variation for breeding. And the extensive lower LD indicates the higher resolution of association mapping in GEM derived materials

Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974

Disciplines Derived from the Discovery of Historical Archives

This paper reviews four famous areas of study in China that emerged from the discovery of historical archives to become their own disciplines. Through a literature review, this paper analyzes the importance of historical archives for forming disciplines by introducing the origin, development, research objectives and research contents of these four disciplines. Based on that, this paper finally suggests that people’s attention to archives is the biggest reason for forming disciplines. By discussing the reasons for accelerating the combination of studies and historical archives, this paper gives suggestions for archives management

The Value and Problems of Digital Preservation for Historical Documents in China

China has been taking on large-scale digitization of historical documents. This paper summarizes four advantages of the digital preservation of historical documents from the practice of Chinese archives. First, it is good for protecting the originals; second, it makes the historical documents more convenient to use; third, it lays a resource foundation for the construction of digital archives; last, it is beneficial for utilizing historical documents. This paper also points out problems in China\u27s approach to the digital preservation of historical documents. First, it has been proceeding without a plan. Such a plan should be created by following the Value Principle and Use Principle. Second, the digitized documents have not been explored, which suggests a need for improvement in publicity and access

A learned conservative semi-Lagrangian finite volume scheme for transport simulations

Semi-Lagrangian (SL) schemes are known as a major numerical tool for solving transport equations with many advantages and have been widely deployed in the fields of computational fluid dynamics, plasma physics modeling, numerical weather prediction, among others. In this work, we develop a novel machine learning-assisted approach to accelerate the conventional SL finite volume (FV) schemes. The proposed scheme avoids the expensive tracking of upstream cells but attempts to learn the SL discretization from the data by incorporating specific inductive biases in the neural network, significantly simplifying the algorithm implementation and leading to improved efficiency. In addition, the method delivers sharp shock transitions and a level of accuracy that would typically require a much finer grid with traditional transport solvers. Numerical tests demonstrate the effectiveness and efficiency of the proposed method.Comment: 24 page

Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation

Glaucoma is one of the leading causes of irreversible blindness. Segmentation of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in glaucoma screening. Although many deep learning models have been constructed for this task, it remains challenging to train an OD/OC segmentation model that could be deployed successfully to different healthcare centers. The difficulties mainly comes from the domain shift issue, i.e., the fundus images collected at these centers usually vary greatly in the tone, contrast, and brightness. To address this issue, in this paper, we propose a novel unsupervised domain adaptation (UDA) method called Reconstruction-driven Dynamic Refinement Network (RDR-Net), where we employ a due-path segmentation backbone for simultaneous edge detection and region prediction and design three modules to alleviate the domain gap. The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way. It also uses a style-consistency constraint to force the network to retain more domain-invariant information. The low-level feature refinement (LFR) module employs input-specific dynamic convolutions to suppress the domain-variant information in the obtained low-level features. The prediction-map alignment (PMA) module elaborates the entropy-driven adversarial learning to encourage the network to generate source-like boundaries and regions. We evaluated our RDR-Net against state-of-the-art solutions on four public fundus image datasets. Our results indicate that RDR-Net is superior to competing models in both segmentation performance and generalization abilit