Explore the vast range of titles available.

Jiayang Li, Xudong Zhu, Qian Qian and colleagues report cloning of the Grain Length on Chromosome 7 ( GL7 ) locus in rice and identify a copy number variant that increases grain length and improves grain quality. They demonstrate how interactions with other grain length–related genes may be used to improve breeding. Copy number variants (CNVs) are associated with changes in gene expression levels and contribute to various adaptive traits 1 , 2 . Here we show that a CNV at the Grain Length on Chromosome 7 ( GL7 ) locus contributes to grain size diversity in rice ( Oryza sativa L.). GL7 encodes a protein homologous to Arabidopsis thaliana LONGIFOLIA proteins, which regulate longitudinal cell elongation. Tandem duplication of a 17.1-kb segment at the GL7 locus leads to upregulation of GL7 and downregulation of its nearby negative regulator, resulting in an increase in grain length and improvement of grain appearance quality. Sequence analysis indicates that allelic variants of GL7 and its negative regulator are associated with grain size diversity and that the CNV at the GL7 locus was selected for and used in breeding. Our work suggests that pyramiding beneficial alleles of GL7 and other yield- and quality-related genes may improve the breeding of elite rice varieties.

Human breast cancers that have HER2 amplification/overexpression frequently carry PIK3CA mutations, and are often associated with a worse prognosis. However, the role of PIK3CA mutations in the initiation and maintenance of these breast cancers remains elusive. In the present study, we generated a compound mouse model that genetically mimics HER2-positive breast cancer with coexisting PIK3CA H1047R . Induction of PIK3CA H1047R expression in mouse mammary glands with constitutive expression of activated Her2/Neu resulted in accelerated mammary tumorigenesis with enhanced metastatic potential. Interestingly, inducible expression of mutant PIK3CA resulted in a robust activation of phosphatidylinositol-3-kinase (PI3K)/AKT signaling but attenuation of Her2/Her3 signaling, and this can be reversed by deinduction of PIK3CA H1047R expression. Strikingly, although these Her2 + PIK3CA H1047R -initiated primary mammary tumors are refractory to HER2-targeted therapy, all tumors responded to inactivation of the oncogenic PIK3CA H1047R , a situation closely mimicking the use of a highly effective inhibitor specifically targeting the mutant PIK3CA/p110a. Notably, these tumors eventually resumed growth, and a fraction of them escaped PI3K dependence by compensatory ERK activation, which can be blocked by combined inhibition of Her2 and MEK. Together, these results suggest that PIK3CA-specific inhibition as a monotherapy followed by combination therapy targeting MAPK and HER2 in a timely manner may be an effective treatment approach against HER2-positive cancers with coexisting PIK3CA -activating mutations.

Wheat stripe rust, caused by an obligate biotrophic pathogen Puccinia striiformis f. sp. tritici ( Pst ) seriously threatens wheat production. Discovering and utilizing of wheat resistance genes is the most effective and economical method to control diseases. The G-type lectin receptor-like kinase (LecRLKs) involved in biotic stress perception, while their roles in wheat resistance to Pst remain elusive. In our study, we identified 398 G-type LecRKs in wheat through BLAST and HMM profiling. The transcript level of 16 random selected G-type LecRKs from each subfamily were analyzed and found TaSRLK is highly induced by avirulent Pst CYR23 infection. TaSRLK-silenced wheat plants showed reduced resistance to Pst with increased hyphal length and decreased H 2 O 2 accumulation. Surprisingly, TaSRLK was localized to the chloroplast and can induce cell death in Nicotiana benthamiana . Further, TaSRLK was shown to interact with and phosphorylate a peroxidase TaPrx1. Importantly, TaPrx1 involved in wheat resistance to Pst through regulating reactive oxygen species (ROS) production. Together these findings demonstrate that TaSRLK positively modulates ROS-associated wheat resistance by binding with TaPrx1.

In this paper, we propose relative projective differential invariants (RPDIs) which are invariant to general projective transformations. By using RPDIs and the structural frame of integral invariant, projective weighted moment invariants (PIs) can be constructed very easily. It is first proved that a kind of projective invariants exists in terms of weighted integration of images, with relative differential invariants as the weight functions. Then, some simple instances of PIs are given. In order to ensure the stability and discriminability of PIs, we discuss how to calculate partial derivatives of discrete images more accurately. Since the number of pixels in discrete images before and after the geometric transformation may be different, we design the method to normalize the number of pixels. These ways enhance the performance of PIs. Finally, we carry out some experiments based on synthetic and real image datasets. We choose commonly used moment invariants for comparison. The results indicate that PIs have better performance than other moment invariants in image retrieval and classification. With PIs, one can compare the similarity between images under the projective transformation without knowing the parameters of the transformation, which provides a good tool to shape analysis in image processing, computer vision and pattern recognition.

Geometric moment invariants (GMIs) have been widely used as basic tool in shape analysis and information retrieval. Their structure and characteristics determine efficiency and effectiveness. Two fundamental building blocks or generating functions (GFs) for invariants are discovered, which are dot product and vector product of point vectors in Euclidean space. The primitive invariants (PIs) can be derived by carefully selecting different products of GFs and calculating the corresponding multiple integrals, which translates polynomials of coordinates of point vectors into geometric moments. Then the invariants themselves are expressed in the form of product of moments. This procedure is just like DNA encoding proteins. All GMIs available in the literature can be decomposed into linear combinations of PIs. This paper shows that Hu's seven well known GMIs in computer vision have a more deep structure, which can be further divided into combination of simpler PIs. In practical uses, low order independent GMIs are of particular interest. In this paper, a set of PIs for similarity transformation and affine transformation in 2D are presented, which are simpler to use, and some of which are newly reported. The discovery of the two generating functions provides a new perspective of better understanding shapes in 2D and 3D Euclidean spaces, and the method proposed can be further extended to higher dimensional spaces and different manifolds, such as curves, surfaces and so on.