Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.

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Long intergenic non-coding RNAs (lincRNAs) are important regulators of cellular processes, including development and stress response. Many lincRNAs have been bioinformatically identified in plants, but their evolutionary dynamics and expression characteristics are still elusive. Here, we systematically identified thousands of lincRNAs in 26 plant species, including 6 non-flowering plants, investigated the conservation of the identified lincRNAs in different levels of plant lineages based on sequence and/or synteny homology and explored characteristics of the conserved lincRNAs during plant evolution and their co-expression relationship with protein-coding genes (PCGs). In addition to confirmation of the features well documented in literature for lincRNAs, such as species-specific, fewer exons, tissue-specific expression patterns and less abundantly expressed, we revealed that histone modification signals and/or binding sites of transcription factors were enriched in the conserved lincRNAs, implying their biological functionalities, as demonstrated by identifying conserved lincRNAs related to flower development in both the Brassicaceae and grass families and ancient lincRNAs potentially functioning in meristem development of non-flowering plants. Compared to PCGs, lincRNAs are more likely to be associated with transposable elements (TEs), but with different characteristics in different evolutionary lineages, for instance, the types of TEs and the variable level of association in lincRNAs with different conservativeness. Together, these results provide a comprehensive view on the evolutionary landscape of plant lincRNAs and shed new insights on the conservation and functionality of plant lincRNAs.

Long intergenic non-coding RNAs (lincRNAs) have been demonstrated to be vital regulators of diverse biological processes in both animals and plants. While many lincRNAs have been identified in cotton, we still know little about the repositories and conservativeness of lincRNAs in different cotton species or about their role in responding to biotic stresses. Here, by using publicly available RNA-seq datasets from diverse sources, including experiments of Verticillium dahliae (Vd) infection, we identified 24,425 and 17,713 lincRNAs, respectively, in Gossypium hirsutum (Ghr) and G. barbadense (Gba), the two cultivated allotetraploid cotton species, and 6933 and 5911 lincRNAs, respectively, in G. arboreum (Gar) and G. raimondii (Gra), the two extant diploid progenitors of the allotetraploid cotton. While closely related subgenomes, such as Ghr_At and Gba_At, tend to have more conserved lincRNAs, most lincRNAs are species-specific. The majority of the synthetic and transcribed lincRNAs (78.2%) have a one-to-one orthologous relationship between different (sub)genomes, although a few of them (0.7%) are retained in all (sub)genomes of the four species. The Vd responsiveness of lincRNAs seems to be positively associated with their conservation level. The major functionalities of the Vd-responsive lincRNAs seem to be largely conserved amongst Gra, Ghr, and Gba. Many Vd-responsive Ghr-lincRNAs overlap with Vd-responsive QTL, and several lincRNAs were predicted to be endogenous target mimicries of miR482/2118, with a pair being highly conserved between Ghr and Gba. On top of the confirmation of the feature characteristics of the lincRNAs previously reported in cotton and other species, our study provided new insights into the conservativeness and divergence of lincRNAs during cotton evolution and into the relationship between the conservativeness and Vd responsiveness of lincRNAs. The study also identified candidate lincRNAs with a potential role in disease response for functional characterization.

Pseudoregma bambucicola is an economically significant bamboo pest which is highly reproductive found in Eastern Asia that influences the normal growth of bamboo, induces the formation of sooty molds, and even causes substantial injury or death among young bamboos. This study provides a theoretical and practical basis for the comprehensive prevention and control of Pseudoregma bambucicola. The results showed that 10 geographical populations of Pseudoregma bambucicola showed high genetic diversity (Hd = 0.879 PI + 0.075, =0.08295 + 0.03901) and the average genetic distance was 0.018. In the haplotype phylogenetic tree constructed, the branches of MP tree and BI tree were basically the same, the Sichuan Basin was clustered into one and the populations from other provinces were clustered into a large branch and had a high posterior probability (the posterior probability of MP tree was 99% and the posterior probability of BI tree was 1). The NJ tree showed the differences among the 10 geographies. It was found that the experimental results were consistent with the actual geographical conditions because different geographical Pseudoregma bambusicola experienced natural isolation due to the possible impact of the natural environment, resulting in no genetic exchange between them, thus having a great genetic variability and high genetic diversity.