1,610 research outputs found
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
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