CrossHomo: Cross-Modality and Cross-Resolution Homography Estimation.

Multi-modal homography estimation aims to spatially align the images from different modalities, which is quite challenging since both the image content and resolution are variant across modalities. In this paper, we introduce a novel framework namely CrossHomo to tackle this challenging problem. Our framework is motivated by two interesting findings which demonstrate the mutual benefits between image super-resolution and homography estimation. Based on these findings, we design a flexible multi-level homography estimation network to align the multi-modal images in a coarse-to-fine manner. Each level is composed of a multi-modal image super-resolution (MISR) module to shrink the resolution gap between different modalities, followed by a multi-modal homography estimation (MHE) module to predict the homography matrix. To the best of our knowledge, CrossHomo is the first attempt to address the homography estimation problem with both modality and resolution discrepancy. Extensive experimental results show that our CrossHomo can achieve high registration accuracy on various multi-modal datasets with different resolution gaps. In addition, the network has high efficiency in terms of both model complexity and running speed. The source codes are available at https://github.com/lep990816/CrossHomo.

Genetic variation in ZmWAX2 confers maize resistance to Fusarium verticillioides.

Fusarium verticillioides (F. verticillioides) is a widely distributed phytopathogen that incites multiple destructive diseases in maize, posing a grave threat to corn yields and quality worldwide. However, there are few reports of resistance genes to F. verticillioides. Here, we reveal that a combination of two single nucleotide polymorphisms (SNPs) corresponding to ZmWAX2 gene associates with quantitative resistance variations to F. verticillioides in maize through a genome-wide association study. A lack of ZmWAX2 compromises maize resistance to F. verticillioides-caused seed rot, seedling blight and stalk rot by reducing cuticular wax deposition, while the transgenic plants overexpressing ZmWAX2 show significantly increased immunity to F. verticillioides. A natural occurrence of two 7-bp deletions within the promoter increases ZmWAX2 transcription, thus enhancing maize resistance to F. verticillioides. Upon Fusarium stalk rot, ZmWAX2 greatly promotes the yield and grain quality of maize. Our studies demonstrate that ZmWAX2 confers multiple disease resistances caused by F. verticillioides and can serve as an important gene target for the development of F. verticillioides-resistant maize varieties.

Interpretable Multi-modal Image Registration Network Based on Disentangled Convolutional Sparse Coding.

Multi-modal image registration aims to spatially align two images from different modalities to make their feature points match with each other. Captured by different sensors, the images from different modalities often contain many distinct features, which makes it challenging to find their accurate correspondences. With the success of deep learning, many deep networks have been proposed to align multi-modal images, however, they are mostly lack of interpretability. In this paper, we first model the multi-modal image registration problem as a disentangled convolutional sparse coding (DCSC) model. In this model, the multi-modal features that are responsible for alignment (RA features) are well separated from the features that are not responsible for alignment (nRA features). By only allowing the RA features to participate in the deformation field prediction, we can eliminate the interference of the nRA features to improve the registration accuracy and efficiency. The optimization process of the DCSC model to separate the RA and nRA features is then turned into a deep network, namely Interpretable Multi-modal Image Registration Network (InMIR-Net). To ensure the accurate separation of RA and nRA features, we further design an accompanying guidance network (AG-Net) to supervise the extraction of RA features in InMIR-Net. The advantage of InMIR-Net is that it provides a universal framework to tackle both rigid and non-rigid multi-modal image registration tasks. Extensive experimental results verify the effectiveness of our method on both rigid and non-rigid registrations on various multi-modal image datasets, including RGB/depth images, RGB/near-infrared (NIR) images, RGB/multi-spectral images, T1/T2 weighted magnetic resonance (MR) images and computed tomography (CT)/MR images. The codes are available at https://github.com/lep990816/Interpretable-Multi-modal-Image-Registration.

Evaluation and Identification of Resistance Lines and QTLs of Maize to Seedborne Fusarium verticillioides.

Internal fungal contamination in cereal grains may affect plant growth and result in health concerns for humans and animals. Fusarium verticillioides is a seedborne fungus that can systemically infect maize. However, few efforts had been devoted to studying the genetics of maize resistance to seedborne F. verticillioides. In this study, we developed a disease evaluation method to identify resistance to seedborne F. verticillioides in maize, by which a set of 121 diverse maize inbred lines were evaluated. A 160 F10-generation recombinant inbred line (RIL) population derived from a cross of the resistant (BT-1) and susceptible (N6) inbred line was further used to identify major quantitative trait loci (QTLs) for seedborne F. verticillioides resistance. Eighteen inbred lines with a high resistance to seedborne F. verticillioides were characterized and could be used as potential germplasm resources for genetic improvement of maize resistance. Six QTLs with high heritability across multiple environments were detected on chromosomes 3, 4, 6, and 10, among which was a major QTL, qISFR4-1. Located on chromosome 4 at the interval of 12922609-13418025, qISFR4-1 could explain 16.63% of the total phenotypic variance. Distinct expression profiles of eight candidate genes in qISFR4-1 between BT-1 and N6 inbred lines suggested their pivotal regulatory roles in seedborne F. verticillioides resistance. Taken together, these results will improve our understanding of the resistant mechanisms of seedborne F. verticillioides and would provide valuable germplasm resources for disease resistance breeding in maize.