Multiview Learning for Impervious Surface Mapping Using High-Resolution Multispectral Imagery and LiDAR Data

The use of multisource remote sensing data to obtain urban impervious surface has become a popular research topic. Multisource remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association between similar object data. To fill this gap, in this article, we propose a dual-view learning fusion classification method (DvLF) based on multiview learning. First, DvLF uses cotraining algorithm to combine multiple data sources for accurate classification, extracting easy-to-classify area while separating difficult-to-classify regions for further analysis. Second, a canonical correlation analysis method is adopted to mine the degree of association of similar object data for constructing a subspace projection field of each object sample. The data in the difficult-to-classify regions are classified in the projection field of each object, and then the results of each classification are fused by voting. Finally, the classification results of the two regions are combined into the classification results of the whole image to achieve impervious surface mapping. The proposed method is applied to the dual-sensor (high-resolution image and LiDAR) Buffalo dataset and the dual-sensor (RGB and multispectral LiDAR) Houston dataset. The experimental results show that our method achieved a significant improvement in classification accuracy compared to other methods. The overall classification accuracy of this new DvLF fusion method on the Buffalo and Houston datasets is 83.35% and 88.84%, respectively, leading to accurate high-resolution impervious surface mapping

Genetic Parameters and Genetic Progress of Growth Traits in a Landrace Pig Population

【Objective】Four genetic parameters including days to 100 kg (DAYS_100), average daily gain (ADG_100), loin muscle area (LMA_100) and average back fat thickness (BFT_100) at 100 kg body weight in a Landrace pig population were estimated, and the correlations between traits as well as genetic and phenotypic progress of the four traits were analyzed, which could provide a basis for the genetic improvement of the target population.【Method】Records of growth traits of Landrace pigs were collected in a core breeding pig farm in Guangxi from 2002 to 2020. A fixed effect analysis on the factors affecting the growth traits of Landrace pigs was conducted by R software. In addition, the genetic parameters of the four traits were estimated with DMU software and a multi-trait animal model. Furthermore, the genetic correlations and phenotypic correlations between these traits, genetic progress and phenotypic progress were evaluated.【Result】The estimated heritability for the four growth traits of Landrace pigs, including DAYS_100, ADG_100, LMA_100 and BFT_100 were 0.399, 0.391, 0.433 and 0.421, respectively, and all of them had medium to high heritability. Both genetic correlation and phenotypic correlation between DAYS_100 and ADG_100 were significantly negative, with correlation coefficient -0.997 and -0.992, respectively. In general, the phenotypic trend of DAYS_100 was rising while the phenotypic trends of ADG_100, LMA_100 and BFT_100 were declining; the genetic trends of ADG_100 and BFT_100 showed an overall upward trend while the trends of DAYS_100 and LMA_100 were generally downward.【Conclusion】The four growth traits of Landrace pigs are medium-high heritability traits, therefore, their genetic progress can be accelerated through direct selection. There is a strong correlation between DAYS_100 and ADG_100. The management of phenotypic measurement of pig farms and the selection of target traits for pig population breeding have an important impact on the performance of growth traits. In addition, the improvements in farm production management and changes in breed structure may influence genetic progress

Sparse Reconstruction for Bioluminescence Tomography Based on the Semigreedy Method

Bioluminescence tomography (BLT) is a molecular imaging modality which can three-dimensionally resolve the molecular processes in small animals in vivo. The ill-posedness nature of BLT problem makes its reconstruction bears nonunique solution and is sensitive to noise. In this paper, we proposed a sparse BLT reconstruction algorithm based on semigreedy method. To reduce the ill-posedness and computational cost, the optimal permissible source region was automatically chosen by using an iterative search tree. The proposed method obtained fast and stable source reconstruction from the whole body and imposed constraint without using a regularization penalty term. Numerical simulations on a mouse atlas, and in vivo mouse experiments were conducted to validate the effectiveness and potential of the method

Genomic insight into the origin, domestication, dispersal, diversification and human selection of Tartary buckwheat

Background Tartary buckwheat, Fagopyrum tataricum, is a pseudocereal crop with worldwide distribution and high nutritional value. However, the origin and domestication history of this crop remain to be elucidated. Results Here, by analyzing the population genomics of 567 accessions collected worldwide and reviewing historical documents, we find that Tartary buckwheat originated in the Himalayan region and then spread southwest possibly along with the migration of the Yi people, a minority in Southwestern China that has a long history of planting Tartary buckwheat. Along with the expansion of the Mongol Empire, Tartary buckwheat dispersed to Europe and ultimately to the rest of the world. The different natural growth environments resulted in adaptation, especially significant differences in salt tolerance between northern and southern Chinese Tartary buckwheat populations. By scanning for selective sweeps and using a genome-wide association study, we identify genes responsible for Tartary buckwheat domestication and differentiation, which we then experimentally validate. Comparative genomics and QTL analysis further shed light on the genetic foundation of the easily dehulled trait in a particular variety that was artificially selected by the Wa people, a minority group in Southwestern China known for cultivating Tartary buckwheat specifically for steaming as a staple food to prevent lysine deficiency. Conclusions This study provides both comprehensive insights into the origin and domestication of, and a foundation for molecular breeding for, Tartary buckwhea