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Enhanced rice breeding with GLR1_CAPS marker for glabrous hull selection
Background
Rice is the primary source of calories for a large portion of the global population. Most rice hulls are of the trichome-type and produce dust, which can cause respiratory allergies and environmental concerns during handling. By contrast, glabrous-type hulls reduce storage volume and minimise dust, making processing cleaner and more eco-friendly. Despite the advantages of glabrous rice as a breeding resource, no molecular markers are available for the effective selection of this trait.
Results
In this study, we developed a novel cleaved amplified polymorphic sequence (CAPS) marker specifically designed to select glabrous hulls in rice. Using the sequence information of the Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) marker KJ05_001, closely linked to
GLR1
, we identified an
Mse
I recognition site for targeted digestion. This sequence was used to create the
GLR1
_CAPS marker, showing tight linkage to
GLR1
. The effectiveness of this marker was tested in 290 diverse rice germplasm lines, confirming its broad applicability.
Conclusions
Overall, the
GLR1
_CAPS marker is an efficient and reliable tool for breeding programs focused on the development of glabrous rice varieties. By facilitating accurate selection of this trait, the developed marker offers substantial improvements in rice storage, handling, and processing, contributing to more sustainable and allergy-friendly agricultural practices. This novel molecular marker represents an important advancement in rice breeding and opens new avenues for the development of rice varieties with reduced environmental and health impacts.
Graphical Abstract
Journal Article
Jane Addams : social worker and Nobel Peace Prize winner
Describes the life of the woman whose devotion to social work led to her establishing Hull House in Chicago and who was awarded the Nobel Peace Prize in 1931.
Book
Feature-based rapid reconstruction method for hull plate of ship block
The construction accuracy of block hull plates is the key to improving the non-allowance construction rate of ships, and the accuracy inspection during its forming process provides a data basis for ensuring construction accuracy. The existing accuracy measurement of block hull plates relying on total stations still has problems such as low detection efficiency and poor timeliness. Therefore, a rapid reconstruction method for hull block plates based on the point cloud characteristics of hull curved plates is proposed. Firstly, based on geometric attributes, a method for extracting the normal vectors of the boundary points of the curved plate point cloud is proposed, and then a curved plate point cloud splicing method based on the node interpolation algorithm is created. Finally, the rapid reconstruction of the side and stern block hull plates of a bulk carrier is taken as an example to verify the effectiveness of the proposed method.
Journal Article
The house that Jane built : a story about Jane Addams
\"This is the story of Jane Addams, the first American woman to receive the Nobel Peace Prize, who transformed a poor neighborhood in Chicago by opening up her house as a community center.\"--Amazon.com.
Book
Geometric approach to asymptotic expansion of Feynman integrals
We present an algorithm that reveals relevant contributions in non-threshold-type asymptotic expansion of Feynman integrals about a small parameter. It is shown that the problem reduces to finding a convex hull of a set of points in a multidimensional vector space.
Journal Article
A comprehensive analysis of autocorrelation and bias in home range estimation
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation (N̂area) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂area >1,000, where 30% had an N̂area <30. In this frequently encountered scenario of small N̂area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
Journal Article
Limit theory for the first layers of the random convex hull peeling in the unit ball
The convex hull peeling of a point set is obtained by taking the convex hull of the set and repeating iteratively the operation on the interior points until no point remains. The boundary of each hull is called a layer. We study the number of k-dimensional faces and the outer defect intrinsic volumes of the first layers of the convex hull peeling of a homogeneous Poisson point process in the unit ball whose intensity goes to infinity. More precisely we provide asymptotic limits for their expectation and variance as well as a central limit theorem. In particular, the growth rates do not depend on the layer.
Journal Article
Enhanced rice breeding with GLR1_(C)APS marker for glabrous hull selection
Rice is the primary source of calories for a large portion of the global population. Most rice hulls are of the trichome-type and produce dust, which can cause respiratory allergies and environmental concerns during handling. By contrast, glabrous-type hulls reduce storage volume and minimise dust, making processing cleaner and more eco-friendly. Despite the advantages of glabrous rice as a breeding resource, no molecular markers are available for the effective selection of this trait. In this study, we developed a novel cleaved amplified polymorphic sequence (CAPS) marker specifically designed to select glabrous hulls in rice. Using the sequence information of the Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) marker KJ05₀01, closely linked to GLR1, we identified an MseI recognition site for targeted digestion. This sequence was used to create the GLR1_(C)APS marker, showing tight linkage to GLR1. The effectiveness of this marker was tested in 290 diverse rice germplasm lines, confirming its broad applicability. Overall, the GLR1_(C)APS marker is an efficient and reliable tool for breeding programs focused on the development of glabrous rice varieties. By facilitating accurate selection of this trait, the developed marker offers substantial improvements in rice storage, handling, and processing, contributing to more sustainable and allergy-friendly agricultural practices. This novel molecular marker represents an important advancement in rice breeding and opens new avenues for the development of rice varieties with reduced environmental and health impacts.
Journal Article
Research on Environment Intelligent Perception with Depth Camera
Environmental intelligent perception technology is among the key technologies for intelligent mobile robots. This paper proposes an environment intelligent perception scheme based on depth cameras to address mobile robot environments intelligent perception challenges. Firstly, the system initiates with direct filtering and point cloud gridding filtering to eliminate noise interference. Secondly, we employ Euclidean clustering extraction to identify non-ground objects and convex hull method to get their location and size. Finally, for 3D scene reconstruction, utilizing Scale-Invariant Feature Transform (SIFT) operator to extract features and match adjacent intensity images to find the corresponding 3D point set. The Iterative Closest Point (ICP) Algorithm is used to compute camera motion matrices to alignment adjacent point clouds.
Journal Article