Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

A triallelic system of S5 is a major regulator of the reproductive barrier and compatibility of indica-japonica hybrids in rice

Abstract: Hybrid sterility is a major form of postzygotic reproductive isolation. Although reproductive isolation has been a key issue in evolutionary biology for many decades in a wide range of organisms, only very recently a few genes for reproductive isolation were identified. The Asian cultivated rice (Oryza sativa L.) is divided into two subspecies, indica and japonica. Hybrids between indica and japonica varieties are usually highly sterile. A special group of rice germplasm, referred to as wide-compatibility varieties, is able to produce highly fertile hybrids when crossed to both indica and japonica. In this study, we cloned S5, a major locus for indica-japonica hybrid sterility and wide compatibility, using a map-based cloning approach. We show that S5 encodes an aspartic protease conditioning embryo-sac fertility. The indica (S5-i) and japonica (S5-j) alleles differ by two nucleotides. The wide compatibility gene (S5-n) has a large deletion in the N terminus of the predicted S5 protein, causing subcellular mislocalization of the protein, and thus is presumably nonfunctional. This triallelic system has a profound implication in the evolution and artificial breeding of cultivated rice. Genetic differentiation between indica and japonica would have been enforced because of the reproductive barrier caused by S5-i and S5-j, and species coherence would have been maintained by gene flow enabled by the wide compatibility gene.

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

Abstract: This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF). The JL module provides robust saliency feature learning, while the latter is introduced for complementary feature discovery. Comprehensive experiments on four popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the top-1 D3Net model by an average of ~1.9% (S-measure) across six challenging datasets, showing that the proposed framework offers a potential solution for real-world applications and could provide more insight into the cross-modality complementarity task. The code will be available at https://github.com/kerenfu/JLDCF/.

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Abstract: Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

Microstructure and mechanical properties of CoCrFeNiTiAlx high-entropy alloys

Abstract: CoCrFeNiTiAl x ( x values in molar ratio, x = 0, 0.5, 1.0, 1.5 and 2.0) high-entropy alloys were prepared using a vacuum arc melting method. The effects of Al addition on microstructure and mechanical properties were investigated. The results show that only a face-centered cubic (FCC) crystal structure phase is observed in the CoCrFeNiTi alloy. The phase composition transforms to stabilized body-centered cubic (BCC) structure phases and typically cast dendrite structure appears when Al is added. The dendrite region is rich in Co, Ni, Ti and Al elements while the interdendrite region is rich in Fe and Cr elements. Subgrains and nanosized precipitates are observed in the as-cast CoCrFeNiTiAl alloy. These CoCrFeNiTiAl x high-entropy alloys exhibit excellent room-temperature mechanical properties. For CoCrFeNiTiAl 1.0 alloy, the compressive strength and elastic modulus reach as high as 2.28 GPa and 147.6 GPa, respectively. High density of dimple-like structure is observed from the fracture surfaces of the Al 0 alloy, while alloys with Al addition show typical cleavage fractures with river-like patterns and cleavage steps.