Sentence examples for wheat class from inspiring English sources
The Ae. crassa cytoplasm alters expression patterns of wheat class B MADS-box genes at the floral meristem of (cr -CSdt7BS [ 6].
Recently, Shitsukawa et al. [ 39] found a differential contribution of the three homoeologs of a wheat class E MADS box gene (WLHS1), where the A genome WLHS1 homoeolog appeared to be inactivated by an insertion and the B homoeolog was predominantly silenced by cytosine methylation.
Primordia of the pistil-like stamens lack expression of wheat APETALA3 (and) and PISTILLATA (PI) orthologs such as WAP3, WPI-1 and WPI-2 [ 14], whereas two wheat class C MADS-box genes, wheat AGAMOUS paralogs (WAG-1 and WAG-2), are ectopically expressed at the pistil-like stamens [ 15, 16].
Our previous studies revealed that wheat class B MADS-box genes WPI1, WPI2 and WAP3 are downregulated at floral whorl three in alloplasmic wheat lines showing pistillody [ 6, 14], whereas two class C MADS-box genes, WAG-1 and WAG-2, and TaDL, an ortholog of rice DROOPING LEAF (DL), are ectopically expressed in primordia of the pistil-like transformed stamens [ 15, 16, 23, 66].
Visual differentiation of wheat classes suffers from disadvantages such as inconsistency, low throughput, and labour intensiveness.
Differentiation of wheat classes is one of the important challenges to the Canadian grain industry.
Even though some wheat classes may look similar, their chemical composition and consequently the end-product quality can vary significantly.
Digital imaging research has addressed this issue over the past two decades, with success in recognition of differing wheat classes and distinguishing wheat from non-wheat species.
A near-infrared (NIR) hyperspectral imaging system was used to develop classification models to differentiate wheat classes grown in western Canada.
In Quadratic Discriminant Analysis (QDA) with a leave-one-out cross-validation method, the classification accuracies were >86% for all wheat classes.
Seventy-five relative reflectance intensities were extracted from the scanned images and used for the differentiation of wheat classes using a statistical classifier and an artificial neural network (ANN) classifier.
