Genome-wide identification and expression profiling reveal the diverse role of Methyl-CpG-binding domain proteins in tea plant Camellia sinensis

Methyl-CpG-binding domain(MBD)proteins are important DNA methylation readers that recognise methylated CpG sites and recruit histone deacetylase(HDAC)complexes and chromatin remodelling factors,leading to chromatin compaction,gene transcription,and genome integrity.Currently,MBD genes have only been identified in a few plant species and their structure and function in tea plants(Camellia sinensis)are unknown.In this study,16 C.sinensis MBD genes(CsMBD)were identified on a genome-wide level and classified into eight classes.The CsMBD genes were mapped on nine chromosomes in tea plants,and nine pairs of CsMBD genes existed.Based on conserved domain analysis,all of the identified CsMBD proteins contained at least one MBD domain.Expression analyses showed that CsMBD genes were expressed in tissue-and organ-specific patterns.We investigated the expression patterns of CsMBD genes in response to abiotic and biotic stresses and during different plant growth and development stages.Multiple pthytohormone and stress-related cis-acting was evaluated in their promoter region,such as GGTCA,TGACG,ABRE and LTR.Specific CsMBD genes were associated with environmental stresses and developmental stages,with little overlap.Overall,our findings reveal the diverse roles of CsMBD genes under different stress and developmental conditions,highlighting candidate genes for further functional studies on tea plants.

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multiscale Feature Pyramid for Document Layout Analysis

Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neural networks(CNNs)are adopted as the feature extraction networks.In this paper,a hybrid spatial-channel attention network(HSCA-Net)is proposed to improve feature extraction capability by introducing attention mechanism to explore more salient properties within document pages.The HSCA-Net consists of spatial attention module(SAM),channel attention module(CAM),and designed lateral attention connection.CAM adaptively adjusts channel feature responses by emphasizing selective information,which depends on the contribution of the features of each channel.SAM guides CNNs to focus on the informative contents and capture global context information among page objects.The lateral attention connection incorporates SAM and CAM into multiscale feature pyramid network,and thus retains original feature information.The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets such as PubLayNet,ICDAR-POD,and Article Regions.Experimental results demonstrate that HSCA-Net achieves state-of-the-art performance on document layout analysis task.