Genome-wide association study on resistance of cultivated soybean to Fusarium oxysporum root rot in Northeast China.

BACKGROUND: Fusarium oxysporum is a prevalent fungal pathogen that diminishes soybean yield through seedling disease and root rot. Preventing Fusarium oxysporum root rot (FORR) damage entails on the identification of resistance genes and developing resistant cultivars. Therefore, conducting fine mapping and marker development for FORR resistance genes is of great significance for fostering the cultivation of resistant varieties. In this study, 350 soybean germplasm accessions, mainly from Northeast China, underwent genotyping using the SoySNP50K Illumina BeadChip, which includes 52,041 single nucleotide polymorphisms (SNPs). Their resistance to FORR was assessed in a greenhouse. Genome-wide association studies utilizing the general linear model, mixed linear model, compressed mixed linear model, and settlement of MLM under progressively exclusive relationship models were conducted to identify marker-trait associations while effectively controlling for population structure. RESULTS: The results demonstrated that these models effectively managed population structure. Eight SNP loci significantly associated with FORR resistance in soybean were detected, primarily located on Chromosome 6. Notably, there was a strong linkage disequilibrium between the large-effect SNPs ss715595462 and ss715595463, contributing substantially to phenotypic variation. Within the genetic interval encompassing these loci, 28 genes were present, with one gene Glyma.06G088400 encoding a protein kinase family protein containing a leucine-rich repeat domain identified as a potential candidate gene in the reference genome of Williams82. Additionally, quantitative real-time reverse transcription polymerase chain reaction analysis evaluated the gene expression levels between highly resistant and susceptible accessions, focusing on primary root tissues collected at different time points after F. oxysporum inoculation. Among the examined genes, only this gene emerged as the strongest candidate associated with FORR resistance. CONCLUSIONS: The identification of this candidate gene Glyma.06G088400 improves our understanding of soybean resistance to FORR and the markers strongly linked to resistance can be beneficial for molecular marker-assisted selection in breeding resistant soybean accessions against F. oxysporum.

Genome-wide association study of powdery mildew resistance in cultivated soybean from Northeast China.

Powdery mildew (PMD), caused by the pathogen Microsphaera diffusa, leads to substantial yield decreases in susceptible soybean under favorable environmental conditions. Effective prevention of soybean PMD damage can be achieved by identifying resistance genes and developing resistant cultivars. In this study, we genotyped 331 soybean germplasm accessions, primarily from Northeast China, using the SoySNP50K BeadChip, and evaluated their resistance to PMD in a greenhouse setting. To identify marker-trait associations while effectively controlling for population structure, we conducted genome-wide association studies utilizing factored spectrally transformed linear mixed models, mixed linear models, efficient mixed-model association eXpedited, and compressed mixed linear models. The results revealed seven single nucleotide polymorphism (SNP) loci strongly associated with PMD resistance in soybean. Among these, one SNP was localized on chromosome (Chr) 14, and six SNPs with low linkage disequilibrium were localized near or in the region of previously mapped genes on Chr 16. In the reference genome of Williams82, we discovered 96 genes within the candidate region, including 17 resistance (R)-like genes, which were identified as potential candidate genes for PMD resistance. In addition, we performed quantitative real-time reverse transcription polymerase chain reaction analysis to evaluate the gene expression levels in highly resistant and susceptible genotypes, focusing on leaf tissues collected at different times after M. diffusa inoculation. Among the examined genes, three R-like genes, including Glyma.16G210800, Glyma.16G212300, and Glyma.16G213900, were identified as strong candidates associated with PMD resistance. This discovery can significantly enhance our understanding of soybean resistance to PMD. Furthermore, the significant SNPs strongly associated with resistance can serve as valuable markers for genetic improvement in breeding M. diffusa-resistant soybean cultivars.

Neural Network With a Preference Sampling Paradigm for Imbalanced Data Classification.

Most data in real life are characterized by imbalance problems. One of the classic models for dealing with imbalanced data is neural networks. However, the data imbalance problem often causes the neural network to display negative class preference behavior. Using an undersampling strategy to reconstruct a balanced dataset is one of the methods to alleviate the data imbalance problem. However, most existing undersampling methods focus more on the data or aim to preserve the overall structural characteristics of the negative class through potential energy estimation, while the problems of gradient inundation and insufficient empirical representation of positive samples have not been well considered. Therefore, a new paradigm for solving the data imbalance problem is proposed. Specifically, to solve the problem of gradient inundation, an informative undersampling strategy is derived from the performance degradation and used to restore the ability of neural networks to work under imbalanced data. In addition, to alleviate the problem of insufficient empirical representation of positive samples, a boundary expansion strategy with linear interpolation and the prediction consistency constraint is considered. We tested the proposed paradigm on 34 imbalanced datasets with imbalance ratios ranging from 16.90 to 100.14. The test results show that our paradigm obtained the best area under the receiver operating characteristic curve (AUC) on 26 datasets.

Multi-View Linear Discriminant Analysis Network.

In many real-world applications, an object can be described from multiple views or styles, leading to the emerging multi-view analysis. To eliminate the complicated (usually highly nonlinear) view discrepancy for favorable cross-view recognition and retrieval, we propose a Multi-view Linear Discriminant Analysis Network (MvLDAN) by seeking a nonlinear discriminant and view-invariant representation shared among multiple views. Unlike existing multi-view methods which directly learn a common space to reduce the view gap, our MvLDAN employs multiple feedforward neural networks (one for each view) and a novel eigenvalue-based multi-view objective function to encapsulate as much discriminative variance as possible into all the available common feature dimensions. With the proposed objective function, the MvLDAN could produce representations possessing: 1) low variance within the same class regardless of view discrepancy, 2) high variance between different classes regardless of view discrepancy, and 3) high covariance between any two views. In brief, in the learned multi-view space, the obtained deep features can be projected into a latent common space in which the samples from the same class are as close to each other as possible (even though they are from different views), and the samples from different classes are as far from each other as possible (even though they are from the same view). The effectiveness of the proposed method is verified by extensive experiments carried out on five databases, in comparison with the 19 state-of-the-art approaches.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.