Elite sd1 alleles in japonica rice and their breeding applications in northeast China

The Green Revolution gene sd1 has been used extensively in modern rice breeding,especially in indica cultivars.However,elite sd1 alleles and related germplasm resources used for japonica rice breeding have not been identified,and extensive efforts are needed for japonica rice breeding to obtain new dwarfing sources.Data from MBKbase-Rice revealed seven sd1 haplotypes in indica and four in japonica rice.Two new sd1 alleles were identified in indica rice.In 295 japonica accessions from northeast Asia,except for the weak functional allele SD1-EQ,sd1-r was the major allele,reducing plant height in comparison with SD1-EQ.Japonica germplasm resources carrying reported sd1 alleles were identified by genotype searching and further verified by literature search,genealogical analysis,and d Caps markers.Pedigrees and geographic distribution showed that sd1-r is an excellent allele widely used in northern China and Tohoku in Japan,and sd1-j is commonly used in east China and Kyushu in Japan.Dongnong-and Xiushui-series cultivars carrying sd1-r and sd1-j,respectively,are essential branches of the backbone parents of Chinese japonica rice,Akihikari and Ce21,with the largest number of descendants and derived generations.In semi-dwarf japonica rice breeding,sd1-d was introgressed into Daohuaxiang 2(DHX2).Dwarf and semi-dwarf lines carrying sd1-d were selected and designated as 1279 and 1280,respectively,after withstanding typhoon-induced strong winds and heavy rains in 2020,and are anticipated to become useful intermediate materials for future genetic research and breeding.This work will facilitate the introduction,parental selection,and marker-assisted breeding,and provide a material basis for the next step in identifying favorable genes that selected together with the sd1 alleles in japonica backbone parents.

Optimal structural design for a certain near-space composite propeller of airship using adaptive region division blending model

Near-space airship is a frontier and hotspot in current military research and development,and the near-space composite propeller is the key technology for its development.In order to obtain higher aerodynamic efficiency at an altitude of 22 km,a certain near-space composite propeller is designed as a long and slender aerodynamic shape with a 10 m diameter,which brings many challenges to the composite structure design.The initial design is obtained by the composite structure variable stiffness design method using based on fixed region division blending model.However,it weighs 23.142 kg,exceeding the required 20 kg.In order to meet the structural design requirements of the propeller,a variable stiffness design method using the adaptive region division blending model is proposed in this paper.Compared with the methods using the fixed region division blending model,this method optimizes region division,stacking thickness and stacking sequence in a single level,considering the coupling effect among them.Through a more refined region division,this method can provide a more optimal design for composite tapered structures.Additionally,to improve the efficiency of optimization subjected to manufacturing constraints,a hierarchical penalty function is proposed to quickly filter out the solutions that do not meet manufacturing constraints.The above methods combined with a Genetic Algorithm(GA)using specific encoding are adopted to optimize the near-space composite propeller.The optimal design of the structure weighs 18.831 kg,with all manufacturing constraints and all structural response constraints being satisfied.Compared with the initial design,the optimal design has a more refined region division,and achieves a weight reduction of 18.6%.This demonstrates that a refined region division can significantly improve the mechanical performance of the composite tapered structure.

Shadow regions detection algorithm by adaptive narrowband two-phase Chan-Vese model

An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.

An adaptive region growing algorithm for breast masses in mammograms

This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors.An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper.In order to accommodate different situations of masses,the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy.106 benign and 110 malignant tumors were included in this study.We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns.Then the segmented results were evaluated by the classification accuracy.42 features including age,intensity,shape and texture were extracted from each segmented mass and support vector machine(SVM)was used as a classifier.The classification accuracy was evaluated using the area(A_(z))under the receiver operating characteristic(ROC)curve.It was found that the maximum likelihood analysis achieved an A_(z)value of 0.835,the maximum gradient analysis got an A_(z)value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_(z)was 0.948.In addition,compared with traditional region growing algorithm,our proposed algorithm is more adaptive and provides a better performance for future works.