Spectral-spatial self-attention networks for hyperspectral image classification.

This study presents a spectral-spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods

Superpixel nonlocal weighting joint sparse representation for hyperspectral image classification.

Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training samples

Bayesian gravitation based classification for hyperspectral images.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples

miRNA Expression Profile of Saliva in Subjects of Yang Deficiency Constitution and Yin Deficiency Constitution

Background/Aims: Based on the theory of constitution in Traditional Chinese Medicine (TCM), the Chinese Han population has been classified into nine constitutions. Of these, Yang deficiency constitution mainly exhibit cold intolerance while Yin deficiency constitution mainly exhibit heat intolerance. Some studies have been carried out to explore the modern genetic and biological basis of such constitution classification, but more remains to be done. MicroRNA (miRNA) serves as post-transcriptional regulators of gene expression and may play a role in the classification process. Here, we examined miRNA expression profile of saliva to further improve the comprehensiveness of constitution classification. Methods: Saliva was collected from Chinese Han individuals with Yang deficiency, Yin deficiency and Balanced constitutions (n=5 each), and miRNA expression profile was determined using the Human miRNA OneArray®v7. Based on 1.5 Fold change, means log2|Ratio|≥0.585 and P-value< 0.05, differentially expressed miRNA was screened. Target genes were predicted using DIANA-TarBasev7.0 and analysis of KEGG pathway was carried out using DIANA-mirPathv.3. Results: We found that 81 and 98 differentially expressed miRNAs were screened in Yang deficiency and Yin deficiency constitution, respectively. Among them, 16 miRNAs were identical and the others were unique. In addition, the target genes that are regulated by the unique miRNAs were significantly enriched in 27 and 20 signaling pathways in Yang deficiency and Yin deficiency constitution, respectively. Thyroid hormone signaling pathway is present in both constitutions. These unique miRNAs that regulated target genes of thyroid hormone signaling pathway may be associated with cold intolerance or heat intolerance. Conclusion: The results of our study show that Yang deficiency and Yin deficiency constitutions exhibit systematic differences in miRNA expression profile. Moreover, the distinct characteristics of TCM constitution may be explained, in part, by differentially expressed miRNAs

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Structural Characteristics of the Yangtze-Huaihe Cold Shear Line over Eastern China in Summer

Based on ERA-Interim data from June to July during 1981–2016 and daily meteorological dataset of China Surface Meteorological Stations (V3.0), 10 typical Yangtze-Huaihe cold shear lines (YCSL) over eastern China (28°~34° N, 110°~122° E) in summer are selected, and the structural characteristics of the YCSL during the evolution process are investigated by the composite analysis. The results indicate that the YCSL is horizontally in a northeast–southwest direction and vertically inclines northward from the lower layer to the upper layer. The vertical extension of the YCSL can reach 750 hPa, and its life time is about 54 h. The evolution process of the YCSL is affected by the comprehensive configuration of the high-level, medium-level, and low-level weather systems. The southward advancement, strengthening, and eastward movement of the north branch low-pressure trough over the Yangtze-Huaihe region at 850 hPa is a key factor for the evolution of the YCSL. Because the structural characteristics of the YCSL have obvious changes in the evolution process, the evolution process can be divided into the development stage, strong stage, and weakening stage. In terms of dynamic structures, the YCSL corresponds well with the axis of the positive vorticity belt, whose center is located at 850 hPa, and reaches the maximum in the strong stage. The YCSL is located in the non-divergence zone, and there are strong convergence centers located on its south side. The YCSL also locates in the ascending motion zone between two secondary circulations on the north and south sides, with the maximum ascending velocity in the strong stage, and its large-value area presents an upright structure. In the development stage, there is an ascending motion along the YCSL, but in the strong and weakening stages there are an ascending motion below 800 hPa and a descending motion above 800 hPa along the YCSL. In terms of thermal structures, the YCSL is located in the low temperature zone of the lower layer, and there is a high temperature zone around 500 hPa. Due to the dominant role of dry and cold airflow from the north, the YCSL locates in the dry and cold air during the development and strong stages, and then the warm and moist airflow from the south invades, resulting in the weakening of the YCSL. There is a convective unstable layer on the south side of the YCSL and a neutral layer on the north side. The water vapor gathers near the YCSL, and there are two water vapor convergence centers on the east and west sides of the YCSL, respectively. The water vapor convergence zone is mainly below 600 hPa in the low troposphere and the convergence center is located at around 900 hPa. The atmospheric baroclinicity is one of the reasons for the northward inclination of the YCSL