In grape breeding programs,the extensive planting of seedlings is a crucial aspect.However,grape seeds display distinct dormancy traits,necessitating a prolonged cold stratification process for dormancy release.In ord...In grape breeding programs,the extensive planting of seedlings is a crucial aspect.However,grape seeds display distinct dormancy traits,necessitating a prolonged cold stratification process for dormancy release.In order to enhance the efficiency of breeding programs,this study presents an innovative in vitro embryo germination technique that eliminates the requirement for cold stratification of seeds.The method involves the disruption of peripheral tissue in grape seed embryos using a straightforward mechanical technique,resulting in the efficient production of a substantial quantity of seed embryos,with a germination rate of up to 88% for these isolated embryos.These embryos are subsequently cultured in vitro to facilitate germination into seedlings,thereby eliminating the need for cold stratification.Consequently,grape seedlings can be obtained within a significantly reduced timeframe of 30-38 d,expediting the overall grape breeding process.This novel approach not only accelerates grape hybridization but also streamlines the selection of new grape varieties,contributing to an efficient and time-sensitive breeding methodology.展开更多
The sparse phase retrieval aims to recover the sparse signal from quadratic measurements. However, the measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel metho...The sparse phase retrieval aims to recover the sparse signal from quadratic measurements. However, the measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel method that combines the quantile regression and the L<sub>1/2</sub>-regularizer. It is a non-convex, non-smooth, non-Lipschitz optimization problem. We propose an efficient algorithm based on the Alternating Direction Methods of Multiplier (ADMM) to solve the corresponding optimization problem. Numerous numerical experiments show that this method can recover sparse signals with fewer measurements and is robust to dense bounded noise and Laplace noise.展开更多
基金supported by Natural Science Foundation of Beijing Academy of Agriculture and Forestry Sciences(Grant No.QNJJ202301)the Youth Research Foundation of Institute of Forestry and Pomology+2 种基金Beijing Academy of Agriculture and Forestry Science(Grant No.LGJJ202202)the BAAFS Funding for the Development of Distinguished Scientist(Grant No.JKZX202402)the Beijing Natural Science Foundation(Grant No.6242019)。
文摘In grape breeding programs,the extensive planting of seedlings is a crucial aspect.However,grape seeds display distinct dormancy traits,necessitating a prolonged cold stratification process for dormancy release.In order to enhance the efficiency of breeding programs,this study presents an innovative in vitro embryo germination technique that eliminates the requirement for cold stratification of seeds.The method involves the disruption of peripheral tissue in grape seed embryos using a straightforward mechanical technique,resulting in the efficient production of a substantial quantity of seed embryos,with a germination rate of up to 88% for these isolated embryos.These embryos are subsequently cultured in vitro to facilitate germination into seedlings,thereby eliminating the need for cold stratification.Consequently,grape seedlings can be obtained within a significantly reduced timeframe of 30-38 d,expediting the overall grape breeding process.This novel approach not only accelerates grape hybridization but also streamlines the selection of new grape varieties,contributing to an efficient and time-sensitive breeding methodology.
文摘The sparse phase retrieval aims to recover the sparse signal from quadratic measurements. However, the measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel method that combines the quantile regression and the L<sub>1/2</sub>-regularizer. It is a non-convex, non-smooth, non-Lipschitz optimization problem. We propose an efficient algorithm based on the Alternating Direction Methods of Multiplier (ADMM) to solve the corresponding optimization problem. Numerous numerical experiments show that this method can recover sparse signals with fewer measurements and is robust to dense bounded noise and Laplace noise.
